What does it mean to run an LLM locally?

Running an LLM locally means the AI model runs on your own computer or server hardware instead of a remote cloud like OpenAI or Anthropic. Your prompts and data never leave the building, the model works with no internet, and there is no monthly per-seat or per-token bill. VYROX sizes the hardware, installs the runtime (Ollama, LM Studio, vLLM or LocalAI) and an open-weight model, and hands it over working.

How much money does a local LLM save versus ChatGPT or Claude?

Cloud AI is a recurring bill that never stops - roughly RM130 to RM280 per user per month for ChatGPT Team or Claude Team, plus metered API tokens for automation. A 20-person team easily spends RM30,000 to RM60,000 a year, every year. A local LLM is a one-time hardware cost (a capable workstation from about RM18,000, a fully-commissioned team server RM22,000 to RM32,000) that then runs unlimited seats for the price of electricity. Most teams break even in 6 to 14 months including setup, then pay effectively nothing.

Which hardware do I need to run a local LLM?

It depends on model size. A 7-14B model runs on a 16GB GPU or an M4 Mac. A 32B model needs a 24-32GB GPU (RTX 3090/4090/5090) or an M4 Pro. A 70B model needs ~48GB (2x 3090/4090, an RTX 6000 Ada, or a 128GB Mac Studio). 120B-class models fit a single RTX PRO 6000 96GB, an A100, a 128GB Mac, or a DGX Spark / AMD Strix Halo mini-PC. 235B and larger need multi-GPU servers, an AMD MI300X, or a high-memory Mac Studio. VYROX sizes it exactly to your models and concurrency.

Which models can I run locally in 2026?

The strongest open-weight models of 2026 include Qwen3.6 (the 27B dense and 35B-A3B MoE), Kimi K2.6 (the leading open agentic coder), GLM-5.1, DeepSeek V4 (and the earlier R1/V3.1), MiniMax M2.7, Xiaomi's MiMo-V2.5-Pro, Google Gemma 4 (writing and vision), plus Llama 4 Scout/Maverick, Mistral Small 3, GPT-OSS 20B and 120B (OpenAI open-weight), Phi-4, and vision models like Qwen3-VL. A 24-32GB GPU runs the best 27-35B-class models at Q4; the trillion-parameter MoEs (Kimi K2.6, DeepSeek V4) run on multi-GPU servers. Quantized to Q4 they match the big cloud models on everyday business work.

Is a local LLM as good as ChatGPT or Claude?

For most business tasks - drafting, summarising, extraction, classification, internal Q&A, coding help and chat - modern open-weight models such as Qwen3.6, Kimi K2.6, GLM-5.1 and DeepSeek V4 run locally at quality very close to the big cloud models. For the hardest frontier reasoning you may still want cloud Claude or GPT for specific jobs; VYROX builds hybrid setups that keep private and high-volume work local and only call the cloud when it genuinely adds value.

What is quantization (Q4, Q8) and which should I use?

Quantization shrinks a model by storing its weights at lower precision. FP16 is full quality at 2 bytes per parameter. Q8 is near-lossless at about half the size. Q4_K_M is the local default sweet spot - roughly a quarter the size of FP16 with only a 2-3% quality drop - so it lets a 70B model fit in about 40GB instead of 140GB. VYROX picks the highest quality quantization your hardware can hold.

Who maintains a local LLM after it is installed?

Every VYROX build ships with remote health monitoring, free model and runtime upgrades, and a same-business-day response SLA. It is built entirely on standard open-source (Ollama, vLLM, Open WebUI) - fully documented, with your IT trained - so there is no black box and no vendor lock-in.

What is the typical payback period for a local LLM build?

Including hardware and setup, most VYROX local builds pay back within 6 to 14 months versus the cloud subscriptions they replace, then run for the price of electricity. The exact crossover date is calculated for your team in the free audit, and is backed by a Break-Even Guarantee.

Can a clinic run a local AI for medical notes (AI Doctor)?

Yes. VYROX builds an air-gapped clinical co-pilot that transcribes consultations into structured SOAP notes (Whisper + a local LLM), summarises patient histories, drafts referral and discharge letters, suggests ICD-10 codes, and retrieves from your own formulary and protocols - with patient data never leaving the clinic, supporting PDPA and medical confidentiality. It is decision-support only: a registered practitioner makes every clinical decision and reviews all output; it is not a diagnostic medical device.

Can an accounting firm run a private local AI (AI Accountant)?

Yes. VYROX builds an on-premise accounting co-pilot that extracts invoices, receipts and bank statements (Qwen3-VL), assists reconciliation, drafts MFRS-aware financial statements and management commentary, runs variance and cash-flow analysis, supports tax computation and LHDN MyInvois/SST workflows, and answers questions over your ledger or AutoCount via MCP - all on your own hardware, supporting MIA client-confidentiality and PDPA. A qualified accountant reviews and signs off; it does not replace professional judgment.

Can a law firm run a private local AI (AI Lawyer)?

Yes. VYROX builds an air-gapped legal co-pilot that reviews contracts and flags risky clauses, drafts agreements and letters from your precedent bank, researches your own matter files with citations (RAG), reviews due-diligence and discovery documents at volume, and summarises long judgments - without a privileged document ever leaving the firm, preserving solicitor-client privilege and supporting LPA/Bar Council confidentiality and PDPA. It assists qualified legal professionals and does not provide legal advice; the advocate and solicitor retains full responsibility.

Can I run agentic AI (autonomous AI agents) locally?

Yes. VYROX builds on-premise agentic AI: autonomous agents that take a goal, plan, call tools via the Model Context Protocol (MCP), act and observe in a loop, and self-correct - all on your own hardware. They use frameworks like LangGraph, CrewAI, AutoGen and OpenHands with tool-calling local models (DeepSeek R1, Qwen3-Coder, Kimi K2, GLM-4.6), plus Computer Use / Browser Use to drive apps that have no API. Every agent ships with human-in-loop approval gates on irreversible actions, sandboxing, full audit logs, a kill switch and an eval harness. Typical use cases: IT helpdesk agents, procurement agents, research agents, coding agents and legacy-app RPA.

What is the difference between chatbot, workflow and agentic AI?

They sit on a spectrum of autonomy. A chatbot is conversational and human-in-every-turn - fastest to deploy (days), grounded in your documents via RAG, ideal for Q&A and support. A workflow is a fixed, auditable multi-step pipeline the model fills in - predictable and repeatable, ideal for high-volume operations like invoice processing or email triage (1-3 weeks to build). An agent is autonomous and goal-driven - it decides its own steps, uses tools and loops until done, ideal for complex 24/7 work (3-8 weeks). All three run on the same local hardware; VYROX helps you start with a chatbot and graduate to workflows and agents as you mature.

Can I automate document processing with a local AI workflow?

Yes. VYROX builds on-premise AI workflows - deterministic pipelines where a trigger feeds documents through extract, validate, classify, route and write-to-system steps, with retries and exception handling. A vision model (Qwen3-VL) reads invoices, receipts and bank statements; the pipeline validates against a schema and writes to your ERP or AutoCount via MCP. Built on n8n, LangGraph, Flowise or Dify, it runs unattended and fully auditable, with your data never leaving your network.

Private Local LLM on Your Own Hardware

Q: Can a TPU or Coral stick run a local LLM?

No. The Google Coral Edge TPU is built for small vision models and cannot run modern LLMs - it has no DRAM and no transformer support. Cloud TPUs and Groq/Cerebras are powerful but are rented cloud services, not on-premise hardware you own. For genuine local LLM hosting the practical accelerators are NVIDIA GPUs, AMD Instinct/Radeon, Apple Silicon, and unified-memory mini-PCs.

Engineered in Kuala Lumpur · deployed across Malaysia & Southeast Asia · MY · SG · TH · ID · PH · VN · BN

OllamavLLMLM StudioOpen WebUIQwen3.6Kimi K2.6GLM-5.1DeepSeek V4Gemma 4Llama 4GPT-OSSNVIDIAApple SiliconAMDLangGraphMCP

10+years building production systems

51+open models we deploy & tune

100%on hardware you own & keep

7motypical payback vs cloud AI

Founded 2015 · led by Ts. Dr. Leong Yee Rock - engineers who size, build & commission the system, then hand it over working. No black box, no lock-in.

Why run AI locally

Three things the cloud can never fully give you.

Running a Large Language Model locally means the AI runs on your own computer or server - not a remote cloud like OpenAI or Anthropic. Your data never leaves the building.

01 / PRIVACY

100% data privacy

Contracts, financials, source code and customer records are processed on a machine you own. Nothing is uploaded, logged by a vendor, or used to train someone else's model. PDPA-aligned by default.

PDPAair-gap optional

02 / OFFLINE

Works fully offline

No internet, no problem. The AI keeps running on the factory floor, in a clinic, at a remote site, or during an outage. No API downtime, no rate limits, no "service unavailable."

0ms WAN depno rate limit

03 / COST

No monthly subscription

Cloud AI charges per user, per month, per token - forever. A local model is a one-time setup that then serves your whole team for the price of electricity. No per-seat licence.

pay onceRM0/seat

Interactive · where your data goes

Cloud sends your data out. Local keeps it in.

Toggle to see the difference.

☁ Cloud AI 🖥 Local (VYROX)

Prompts & documents stay on your server
Nothing sent to OpenAI / Anthropic / Google
No third-party logging or training on your data
Works with the internet unplugged

What you'll actually build

17 things businesses run on a private local LLM.

A local model isn't a chatbot toy - it's the engine behind real systems your team uses daily. Filter by function; each shows the model + tool stack we'd deploy.

All Knowledge & docs Extraction Comms & language Meetings & voice Engineering & data Industry

Chatbot · Workflow · Agentic

Three ways to put your local AI to work.

The same private model on the same hardware runs in three modes - along a spectrum of autonomy. Start with a chatbot for quick wins, add workflows for high-volume operations, then deploy agents for complex 24/7 work.

Chatbot

Workflow

Agentic

← human-driven · simple · instantautonomous · multi-step · 24/7 →

Chatbot AI Workflow AI Agentic AI

Conversational · human-in-every-turn

A private assistant your staff chat with.

The simplest, fastest mode: a ChatGPT-style window grounded in your own documents (RAG). The human reads every answer, so it's low-risk and quick to deploy.

Ask→ Retrieve (RAG)→ Answer + cite→ Human decides

STACK

Open WebUI / LibreChat / AnythingLLM front-end + Qwen3 or Gemma 3 + RAG (nomic-embed + Qdrant) · Ollama / LM Studio

USE CASES

Internal knowledge assistant, customer-support chatbot, HR/policy helper, documentation Q&A, onboarding buddy.

EFFORT

Days to deploy · lowest run-cost · human always in the loop.

At a glance	💬 Chatbot	🔀 Workflow	🤖 Agentic
Autonomy	Low - human every turn	Medium - runs unattended	High - decides its own steps
Path	Free conversation	Fixed pipeline (DAG)	Planned at runtime
Determinism	n/a	High - repeatable	Lower - reasoned each run
Human oversight	Reads every answer	Reviews exceptions	Approves irreversible actions
Build effort	Days	1-3 weeks	3-8 weeks
Run cost (local)	Lowest	Low	Higher (many calls/run)
Best local models	Qwen3.6, Gemma 4, Llama	Qwen3.6, Qwen3-VL, Mistral	Kimi K2.6, GLM-5.1, DeepSeek V4, Qwen3.6
Frameworks	Open WebUI, LibreChat	n8n, LangGraph, Flowise, Dify	LangGraph, CrewAI, AutoGen, OpenHands, MCP
Best for	Q&A, support, knowledge	High-volume repetitive ops	Complex multi-step, 24/7 automation

The mature path: most clients start with a chatbot (value in days), automate their highest-volume process as a workflow, then add agents where the work is genuinely multi-step. All three run on the same local box - and VYROX engineers tool-calling, MCP connectors, guardrails and an eval harness so agents are reliable, not just impressive.

Design my build →

Co-pilots for regulated professions

AI Doctor. AI Accountant. AI Lawyer. On your own hardware.

These three professions handle data that is legally confidential - patient records, privileged files, financial accounts. For them, local AI isn't just cheaper; it's often the only compliant option. Each is a co-pilot that keeps the licensed professional firmly in the loop.

AI Doctor AI Accountant AI Lawyer

For clinics · GPs · specialists · dental · allied health

A clinical co-pilot that never sends a patient record to the cloud.

It listens, drafts and retrieves - so clinicians spend less time on paperwork and more with patients. Every record stays inside the clinic.

Ambient scribe - transcribes the consultation and drafts structured SOAP notes (Whisper + LLM)
History summary - condenses long patient files before each visit
Letters - drafts referral & discharge letters for clinician review
Patient education - leaflets in EN/BM/ZH at the right reading level
Coding assist - suggests ICD-10 codes for claims
Guideline lookup - surfaces references from your own formulary & protocols (RAG)
Front-desk triage - structures symptom intake for the queue

STACK

Whisper large-v3 (transcription) + Qwen3 + RAG over clinical guidelines · air-gapped Desk or Studio build

PRIVACY & COMPLIANCE

Patient data never leaves the clinic - supports PDPA and medical confidentiality; air-gap option for full isolation. MMC-aware.

TYPICAL ROI

Clinicians recover ~1-2 hours/day of documentation time. Build from RM9k-32k, one-time.

⚕️ Decision-support only. A registered medical practitioner makes every clinical decision; the system does not diagnose or treat and is not a registered medical device. It assists documentation and retrieval, with the clinician reviewing all output.

Build my profession's co-pilot →

Connect your own data · RAG

Make the model answer from YOUR files - privately.

A general model knows the public internet. It does not know your contracts, SOPs, pricing or customer history. RAG (Retrieval-Augmented Generation) grounds every answer in your own documents - with citations - and on a local deployment that data never leaves the building.

How it works - 6 steps

Ingest - pull in PDFs, Word, Excel, scans, emails, DB records, intranet pages.
Chunk - split documents into small, meaningful passages for precise retrieval.
Embed - convert each chunk into a numeric fingerprint with a local model (nomic-embed / bge). No cloud calls.
Store - fingerprints go into a local vector database (pgvector, Qdrant or Chroma) on your server.
Retrieve - a question finds the most relevant chunks from your data.
Ground & answer - the local LLM answers using only those chunks, and cites the source.

Why this is the killer use case

Turns a generic model into one that knows your business
Answers carry citations - staff verify, don't just trust
Slashes hallucination: it answers from retrieved facts
Update knowledge by adding files - no retraining

Why local makes it safe

Documents, index and model all sit on your server
Nothing sent to OpenAI, Google or any external API
For patient records, financials, legal files - often the only acceptable answer
PDPA-aligned by design, not by a vendor's promise

Open vs cloud · the quality question

"Is local as good as ChatGPT?" On the work you do - close enough to matter.

The best open models now land within a few points of frontier cloud on knowledge, science and coding benchmarks - and run on hardware you own. Scores are approximate, drawn from public leaderboards and model cards (mid-2026); harnesses differ, so treat ±2-3 points as noise.

Model	Type	MMLU-Pro knowledge	GPQA science	SWE-bench coding

✓ Where open wins for you

On knowledge (MMLU-Pro ~84-90), graduate science (GPQA ~80) and coding (SWE-bench ~65-67), top open models match or beat older cloud models like GPT-4o - running privately on your hardware, with no per-token bill.

⚠ Where cloud still leads

The hardest agentic, long-horizon tasks - large-repo autonomous coding, sustained multi-step planning - still favour the latest frontier Claude/Gemini/OpenAI. That's exactly what our hybrid setups route to the cloud, on demand.

The honest bottom line

For drafting, summarising, extraction, Q&A, translation and everyday coding - the bulk of business work - open models are more than good enough. You keep ~all the quality and stop paying for everything.

Trending local models · updated 2026

The open models worth running - filter by what you do.

Now including the April-May 2026 wave - Qwen3.6, Kimi K2.6, GLM-5.1, DeepSeek V4, MiniMax M2.7 and Gemma 4. Every model below runs locally with Ollama, LM Studio or vLLM. Filter by task, sort by size, and see the VRAM each needs at Q4.

Everything that runs a local LLM

GPUs, Apple Silicon, mini-PCs, servers - the complete map.

The practical accelerators for genuine local LLM hosting. Prices are indicative mid-2026 street estimates (an active DRAM/GPU shortage is inflating prices - we confirm exact figures at quote time).

NVIDIA & AMD GPUs

GPU	VRAM	Bandwidth	Approx RM	Biggest model @ Q4	Class
Intel Arc B580	12 GB	456 GB/s	RM 1.2k-1.6k	7-8B (IPEX/Vulkan)	Budget
RTX 3060	12 GB	360 GB/s	RM 1.3k-1.8k	7-8B	Budget
Intel Arc A770	16 GB	560 GB/s	RM 1.4k-1.9k	13-14B	Budget
RTX 4060 Ti 16GB	16 GB	288 GB/s	RM 2.2k-2.8k	13-14B	Budget
RTX 4070	12 GB	504 GB/s	RM 2.8k-3.2k	13B	Budget
RTX 5070	12 GB	672 GB/s	RM 3k-3.7k	13B	Consumer
RTX 5070 Ti	16 GB	896 GB/s	RM 4.2k-5.5k	14B; GPT-OSS 20B	Consumer
RTX 3090 / Ti	24 GB	936 GB/s	RM 4.1k-6k (used)	32B dense / 30B-A3B	Budget
RTX 4080 / Super	16 GB	717 GB/s	RM 4.6k-6k	14B; GPT-OSS 20B	Consumer
RTX 4090	24 GB	~1008 GB/s	RM 8.7k-12k	32B dense / Gemma 3 27B	Prosumer
RTX 5080	16 GB	~960 GB/s	RM 5.5k-8k	14B; GPT-OSS 20B	Prosumer
RTX 5090	32 GB	1792 GB/s	RM 14k+	32B comfortably; 70B Q3 tight	Prosumer
RTX PRO 5000 Blackwell	48 / 72 GB	1344 GB/s	RM 19k+	70B dense Q4	Workstation
RTX 6000 Ada	48 GB	960 GB/s	RM 31k-37k	70B dense Q4	Workstation
AMD Radeon PRO W7900	48 GB	864 GB/s	RM 16k-18k	70B (ROCm)	Workstation
RTX PRO 6000 Blackwell	96 GB	1792 GB/s	RM 39k-44k	120B-class on ONE card	Workstation
NVIDIA L4	24 GB	300 GB/s	RM 11k+	32B (low-power, slow)	Datacenter
NVIDIA L40S	48 GB	864 GB/s	RM 32k-41k	70B dense Q4	Datacenter
A100 80GB	80 GB	2039 GB/s	RM 41k-69k	120B-class; 235B (2×)	Datacenter
H100	80 GB	3.35 TB/s	RM 115k-147k	120B single; frontier multi	Datacenter
H200	141 GB	~4.8 TB/s	RM 115k-161k	235B single	Datacenter
B200 (Blackwell)	180 GB	~8.0 TB/s	RM 161k+	235B+ single; frontier	Datacenter
AMD Radeon 7900 XTX	24 GB	960 GB/s	RM 4.1k-5k	32B (ROCm/Vulkan)	Budget
AMD Instinct MI300X	192 GB	5.3 TB/s	RM 46k-69k	235B+ single GPU	Datacenter
AMD Instinct MI325X	256 GB	6.0 TB/s	RM 92k+	300B+ single GPU	Datacenter

Multi-GPU note: consumer 40/50-series have no NVLink - GPUs talk over PCIe (tensor-parallel via vLLM, with overhead). 2× 24GB ≈ 48GB → 70B Q4; 4× 3090 ≈ 96GB → 120B-class. A single RTX PRO 6000 96GB often beats multi-GPU on simplicity and power.

Apple Silicon - unified memory advantage

Chip	Max memory	Bandwidth	Product	Approx RM	Biggest model @ Q4
M4	16-32 GB	120 GB/s	Mac Mini / Air	RM 2.8k+	14B-30B-A3B
M4 Pro	64 GB	273 GB/s	Mac Mini Pro / MBP	RM 6.4k+	32B; 70B Q4 tight
M4 Max	128 GB	546 GB/s	Mac Studio / MBP 16	RM 9.2k+	70B dense; GPT-OSS 120B
M5 Max NEW 2026	128 GB	~546 GB/s	Mac Studio / MBP 16	RM 10.5k+	70B dense; faster GPU + NPU, better tok/s
M3 Ultra	256 GB	819 GB/s	Mac Studio	RM 18.4k+	235B-class; Qwen3-235B Q4

Apple's unified memory lets the GPU address all RAM - a cheap path to huge models, limited by bandwidth not capacity. A 256GB M3 Ultra Mac Studio is the most popular single-box big-model machine. (Note: M4 Ultra was never released; Apple withdrew the 512GB option in 2026.)

Mini-PCs & "AI-in-a-box"

Device	Chip	Memory	What it runs	Approx RM
NVIDIA DGX Spark	GB10 Grace-Blackwell	128 GB	up to ~200B Q4; CUDA-native dev box	RM 21.6k
Framework Desktop	Ryzen AI Max+ 395	128 GB unified	70B, GPT-OSS 120B, Qwen3-235B Q4	RM 9.2k-13k
GMKtec / Minisforum / Corsair	Ryzen AI Max+ 395	128 GB	same Strix Halo class	RM 11k-16k
Jetson Thor (AGX)	Blackwell edge	128 GB	70B-class at the edge	RM 16k
Jetson Orin Nano/AGX	Ampere edge	8-64 GB	7B-13B (robotics / CCTV)	RM 1.1k-9.2k

DGX Spark vs Strix Halo: DGX Spark wins on CUDA software compatibility; Strix Halo wins on price (~half) and x86/Linux. Both are bandwidth-limited (~256-273 GB/s) - great for memory-heavy MoE inference, weaker on fast prefill.

TPUs & other accelerators - the honest truth

Accelerator	Local LLM?	Reality
Google Coral Edge TPU	✗ No	Built for tiny vision CNNs. No DRAM, int8 only, no transformer/attention support - cannot run even a 1B LLM.
Google Cloud TPU (Trillium/Ironwood)	⚠ Cloud-only	Powerful for training/serving via JAX, but rented hourly - not on-prem hardware you own.
Groq LPU	⚠ Cloud	Ultra-low-latency inference as a cloud API; real deployments need racks. Not a consumer-local box.
Cerebras WSE-3	✗ No	Wafer-scale, $2M+ datacenter systems. Not local in any SME sense.
Hailo-8/10 NPU	⚠ Niche	Edge vision / very small on-device models only.

Takeaway: for genuine local LLM hosting, the practical accelerators are NVIDIA GPUs, AMD Instinct/Radeon, Apple Silicon, and unified-memory mini-PCs. We'll tell you honestly which fits - never sell you a Coral stick for an LLM.

Multi-GPU servers & rack

Tier	GPUs	Host	Use case	Approx RM
Entry rig	2× RTX 3090/4090	Threadripper, 128GB, 1500W	70B Q4, small team	RM 23k-41k
Prosumer WS	1-2× RTX PRO 6000 96GB	TR PRO, 256GB ECC	120B single-box	RM 55k-100k
4-GPU server	4× L40S / RTX 6000 Ada	Dual EPYC, 512GB-1TB ECC, 4U	235B Q4, multi-user vLLM	RM 184k-322k
8-GPU HGX	8× H100/H200 SXM	NVLink/NVSwitch, 2TB RAM, liquid	Frontier inference + training	RM 1.4M+

Engineering: 8× H100 ≈ 5.6kW (needs 3-phase power); 4U GPU servers are loud (liquid cooling above 4× SXM); EPYC/Threadripper PRO for PCIe 5.0 lanes; platinum/titanium PSUs with N+1 redundancy. VYROX sources, assembles, commissions and supports the whole node.

Will it fit? · interactive

Pick a model. See the VRAM it needs and what runs it.

Choose a model, quantization and context length - we compute the memory and light up the hardware that fits.

Model

Quantization

FP16 Q8 Q6 Q4 Q3

Context length 8K

Concurrent users 1

VRAM / memory required

18 GB

Qwen3-Coder 30BQ4

Quantization explained

Smaller numbers, almost the same brain.

Quantization shrinks a model by storing weights at lower precision. Drag to see size vs quality - Q4 is the local sweet spot.

Precision Q4_K_M

Q4_K_M - the local default. ~4× smaller than FP16 with only ~2-3% quality loss. The best fit-vs-quality tradeoff for almost every build.

Llama 70B: 140 GB → 40 GB

Size

28%

Quality

98%

FP16	2.0 B/param	reference, fine-tuning
Q8	~1.1 B/param	near-lossless
Q4_K_M	~0.6 B/param	sweet spot
Q2	~0.4 B/param	squeeze big models, quality drops

What can I run on MY machine?

Tell us your hardware. We'll list what fits.

Sizing cheat-sheet

Model size → memory → hardware, at a glance.

Rule of thumb: Q4 VRAM (GB) ≈ params(B) × 0.6. MoE models size to total params for memory, but run at the speed of their active params. Always add KV-cache for long context.

Model size	Q4_K_M	Q8	FP16	Example hardware (Q4)
1-3B	~1-2 GB	~3 GB	~6 GB	Any iGPU, phone, Jetson, 8GB GPU
7-8B	~5-6 GB	~9 GB	~16 GB	RTX 4060 8GB, M4 16GB
13-14B	~9-10 GB	~16 GB	~28 GB	RTX 4070/4080, M4
24-32B	~16-20 GB	~34 GB	~65 GB	RTX 3090/4090/5090, M4 Pro
70B dense	~40-43 GB	~75 GB	~140 GB	2× 3090/4090, RTX 6000 Ada, M4 Max
120B MoE (GPT-OSS/Scout)	~60-65 GB	~120 GB	-	RTX PRO 6000 96GB, A100, M3 Ultra, DGX Spark
235B MoE	~135-145 GB	~250 GB	-	2× A100, MI300X, M3 Ultra 256GB
671B (DeepSeek)	~380-400 GB	~700 GB	-	8× H100/H200, multi-MI300X
~1T MoE (Kimi K2.6 / DeepSeek V4)	~550-880 GB	~1-1.6 TB	-	8× H200/B200 server

Context cost: KV-cache grows with context length × layers. A 7-8B model adds ~0.5-1 GB per 8K tokens; a 70B at full 128K context can add 20-40GB+ - often the hidden cost that blows a VRAM budget. We size for weights plus your real context window.

Licensing, power & operations

The details that decide whether you can actually run it.

Open weights don't all mean "free for business." And a multi-GPU box has real power, heat and uptime needs. We handle all of it - here's the honest picture.

Open-weight licensing (commercial use)

License	Models	Commercial use
Apache-2.0	Qwen3 & Qwen3-Coder, GPT-OSS, Mistral Small 3.1 / Nemo / Devstral / Magistral, Gemma-adjacent	Free, permissive - yes
MIT	DeepSeek R1 / V3.1 (+ distills), Phi-4, GLM-4.5/4.6, bge-m3, Whisper	Free, permissive - yes
Gemma	Gemma 3 (1B-27B)	Yes, under Google's Gemma terms (prohibited-use policy applies)
Llama Community / Llama 4	Llama 3.x, Llama 4 Scout / Maverick	Yes - but a special licence is required above 700M monthly active users
Mistral Research (MRL)	Mistral Large 2, Ministral 8B	Research/non-commercial weights - commercial needs a Mistral licence
MNPL (non-production)	Codestral 22B	Not for production without a commercial agreement

VYROX defaults your build to permissively-licensed models (Apache/MIT) so you own your deployment outright - and flags any model with commercial restrictions before it's used.

Power, heat & electrical

Tier	Peak draw	Heat output	Power / cooling	Noise
Desk AI	~0.4-0.6 kW	~2,000 BTU/h	Standard 13A wall socket, room air	Quiet desktop
Studio AI	~0.6-0.9 kW	~3,000 BTU/h	Dedicated 13-15A circuit, ventilated	Low-moderate
Engine AI	~1.2-1.8 kW	~6,000 BTU/h	20A circuit, UPS, server cupboard / aircon	Server-grade fans
Rack AI (8-GPU)	~6-7 kW	~22,000 BTU/h	3-phase power + room cooling / liquid, N+1 PSU	Loud - data-centre/room

A Desk/Studio build sips a few ringgit of electricity a day. Engine tiers want a UPS and a ventilated cupboard. Rack tiers need real facilities - we assess your site and spec power, cooling and UPS as part of delivery.

Networking & access

Staff reach it on your LAN via a browser (Open WebUI) or an OpenAI-compatible API. Remote access over VPN; a reverse proxy + gateway handles auth, SSO and rate-limiting; vLLM load-balances many users across GPUs.

Backup & high availability

Models, the vector DB and configs are backed up and reproducible. For mission-critical use we add a warm spare, a cloud-failover line, or a second node so a single box is never a silent point of failure.

Warranty & support

Hardware carries manufacturer warranty (typically 3 years on workstation/datacentre parts); we handle RMA. Optional managed-service tier adds monitoring, model upgrades and a same-business-day SLA. Leasing / instalment options available.

Feel the speed · interactive

How fast is local, really?

Pick a model and a machine - we'll stream sample text at the estimated tokens/sec so you can feel it.

The money math · interactive

Cloud AI is a bill that never stops. Local is a cost that ends.

Move the sliders to your team. Watch the cumulative cost diverge and find your break-even month.

Team size 20

Cloud cost / person / month RM 180

Years to compare 5

One-time local build (RM) RM 27,000

Build cost auto-suggests from the tier you'd need; drag to match a real quote.

You save over 5 years

RM 138,000

Cloud subscriptionLocal (VYROX)Break-even

Break-even

7 months

Cloud over period

RM 216,000

Illustrative estimate. Cloud ≈ ChatGPT Team / Claude Team seats (~RM130-280/user/mo) at ≈ RM4.6/USD; local figure includes hardware + commissioning; electricity ~RM1-6/day. Your exact crossover is calculated in the free audit.

Honest decision guide

Local, cloud, or hybrid? The straight trade-off.

No single answer is right for everyone. Here's the comparison without the spin - including when cloud or hybrid is genuinely the better call.

Dimension	Local / On-prem	Cloud API	Hybrid
Data privacy	Highest - never leaves you	Lowest - sent to a third party	High - sensitive stays local
Recurring cost	Low & fixed (power + support)	Variable - can balloon	Mixed - base + overflow
Upfront cost	Higher - hardware	Near zero	Moderate
Latency	Low, predictable (your LAN)	Internet + provider load	Depends on path
Offline operation	Yes	No	Partial
Frontier reasoning ceiling	Very good (hardware-capped)	Highest	Best of both
Scaling to spikes	Hardware-limited	Elastic	Burst to cloud
Maintenance	Managed by VYROX	Vendor-managed	Most complex

When cloud is actually better

You need the absolute top frontier model and data isn't sensitive
Usage is very low or unpredictable - hardware would sit idle
You're prototyping before committing to hardware
Sudden massive spikes on-prem can't economically cover

When hybrid wins

Routine work runs local; a few hard tasks need a frontier model
Sensitive data strictly on-prem, non-sensitive bursts to cloud
You're migrating cloud → local and want a gradual cutover
You need an overflow valve for seasonal peaks

Our honest take

For most SMEs handling private or regulated data with steady daily volume, local pays for itself in months and removes per-token billing risk. If cloud or hybrid fits you better, we'll tell you - and build that instead.

Designed builds · costed

Four ready-to-deploy local-AI builds.

Complete, VYROX-commissioned systems - hardware sized, models loaded, runtime and agents wired in, staff trained.

Tier 01 · Solo desk

Desk AI

RM 9k-13k once

Mac Mini M4 Pro 48GB or RTX 4090 24GB + 128GB RAM
Qwen3-Coder 30B, DeepSeek R1 Distill 32B, Gemma 3 27B
~30-45 tok/s single-stream
~1-3 concurrent · team of 3-8
Ollama + Roo Code / Continue.dev

Replaces ~RM12k/yr cloud + Copilot. Payback ~12-18 mo incl. setup.

Tier 02 · Team - popular

Studio AI

RM 22k-32k once

Mac Studio M4 Max 128GB (runs 70B) or RTX 5090 32GB (runs 32B-class)
Qwen3 32B, DeepSeek R1 70B (Mac), GLM 4.5 Air
~60-100 tok/s on 32B single-stream
~5-10 concurrent · team of 20-50
Ollama + Open WebUI + Cline / Aider

Replaces ~RM33k/yr cloud seats. Payback ~10-14 mo - then no per-seat licence.

Tier 03 · Heavy / agents

Engine AI

RM 55k-75k once

2× RTX 5090 (64GB) or RTX PRO 6000 96GB, Threadripper, 256GB
Qwen3.6-35B, GLM-5.1, Kimi K2.6 / DeepSeek V4 (server)
100+ tok/s aggregate (vLLM batching)
~15-30 concurrent · 60-150 staff + agents
vLLM + Open WebUI + agent fleet

Agent token-burn alone runs RM100k+/yr in cloud. Payback ~6-9 mo.

Tier 04 · Org / frontier

Rack AI

RM 180k-500k+ once

4-8× RTX 5090 / RTX PRO 6000 / H100 / H200 / DGX
DeepSeek V3.1 671B, Qwen3-235B, frontier open models
Hundreds tok/s aggregate
~100+ concurrent · whole organisation
vLLM cluster + gateway + SSO + monitoring

Replaces RM300k+/yr enterprise AI - with full data sovereignty.

How "users supported" is calculated: concurrent users = free VRAM after model weights ÷ KV-cache per session (≈ 2 × layers × kv-dim × context × precision), capped by GPU throughput ÷ a 15 tok/s per-user floor and by the runtime's parallel slots (Ollama ~8, vLLM many). "Team size" assumes typical intermittent office use (~1 active generation per 5 staff). Numbers shown are at ~8K context with FP16 KV-cache; Q8 KV-cache roughly doubles concurrency and shorter context increases it further. Use the cost configurator to model your exact model, context and concurrency.

Build advisor · interactive

Tell us your purpose. Get your build, cost and payback.

Pick what you'll use AI for and your team - the recommendation, hardware, model and 5-year savings update instantly.

What will you use AI for? pick all

Coding Research Writing Vision / docs 24/7 agents

People using it 10

Cloud cost / person / mo RM 180

Hardware preference

No preference Apple Mac NVIDIA GPU

Recommended build

Studio AI

A shared server for your team - big-model quality for everyone, fully private.

Hardware	RTX 5090 / Mac Studio
Model	Qwen3 32B / R1 70B
Memory	32-128 GB
Users	~5-10 concurrent
Tools	Ollama + Open WebUI

RM 27k

One-time

11 mo

Break-even

RM 138k

5-yr saved

Get this build quoted free →

Cost configurator · build it your way

Adjust the model, the hardware and the usage. See the full cost.

Two ways to use it: set how many concurrent users you need and leave hardware on Auto - we pick the cheapest build that serves them - or drive every variable yourself (LLM size, quantization, GPU, context, hours/day). The itemised build cost, capacity, payback and cloud savings update live. Indicative estimates; your exact quote comes from the audit.

1 · Primary purpose

Coding Research Writing Vision / docs RAG chatbot 24/7 agents

2 · LLM size 24-32B

3 · Quantization Q4_K_M

4 · Context length 8K

5 · Concurrent users needed 4

On Auto, we size the cheapest build that serves this many at your chosen model & context.

6 · Hardware platform

7 · Usage 8 h/day

8 · Support plan

Self-managed Standard Premium

Cloud you'd otherwise pay · seats 10

Cloud cost / seat / mo RM 180

Compare over 5 yrs

✓Fits - Qwen3 32B class on this build

Users this build supports

~6 concurrent · team of ~30

One-time build

RM 27,000

Payback vs cloud

11 mo

Running / yr

RM 2,400

Performance

~60 t/s

Saved / 5 yrs

RM 138k

Total one-time

RM 27,000

Cumulative cost - cloud vs your build

CloudYour buildBreak-even

Get this exact build quoted free →

Indicative estimate. Hardware ≈ Malaysia street pricing during the 2026 GPU/DRAM shortage; commissioning, electricity (TNB commercial ~RM0.50/kWh) and optional support included as shown. Cloud baseline ≈ Team-tier seats (+ agent API where selected). VYROX confirms exact specs, prices and a measured savings projection in the free audit.

Stop guessing - get the numbers

We'll size your exact build and put the break-even date in writing.

A free 45-minute Local-AI Audit: your real cloud spend today, the right hardware, the models, and the costed payback - no obligation, no sales deck.

Book free audit → Design my build

Security & compliance

"Where does my data go?" Nowhere you don't control.

Built for the privacy-driven buyer. Choose a deployment mode, and layer on the controls your auditor expects.

MODE 01

On-premise server

The LLM runs on a server physically inside your office, factory or data centre. Reachable over your LAN/VPN; the public internet cannot touch it. Best balance of control and convenience.

MODE 02

Air-gapped

No internet connection at all. Updates applied manually via controlled media. For the most sensitive environments - defence-adjacent, critical infrastructure, regulated health/finance.

MODE 03

Private cloud / VPC

Deployed inside your own cloud tenancy or a Malaysian data centre. You keep data residency and isolation, with cloud scalability - local-grade control without owning physical servers.

PDPA alignment	Architected so personal data stays within your control and within Malaysia, supporting your PDPA 2010 obligations.
Data residency	You choose exactly where data physically lives - it can stay entirely on your premises / in Malaysia.
No third-party sharing	No prompts, documents or outputs are sent to any external AI provider. Full stop.
Role-based access (RBAC)	Users and teams only see the data and tools they're permitted to.
Audit logging	Every query and access is logged for traceability and incident review.
Encryption	At rest (documents, vector DB, model data on disk) and in transit (TLS across LAN/VPN).
Single Sign-On	Integrates with Microsoft Entra/Azure AD, Google Workspace, or LDAP.
ISO 27001-aligned process	We follow ISO 27001-aligned practices for access, change and key management during delivery.

VYROX implements ISO 27001-aligned and PDPA-aligned controls and supports your compliance posture; formal certification of your organisation remains with you and your auditor.

RAG vs fine-tuning

Most customers need RAG, not fine-tuning. We'll tell you which.

Use RAG when…

You want the model to know your facts - documents, policies, products, prices. Faster, cheaper, instantly updatable (just add files), and it cites sources. This covers the large majority of business needs.

Fine-tune (LoRA) when…

You need to change the model's behaviour or style - a consistent house tone, a strict output format, domain jargon, or a narrow task it must perform identically every time. RAG adds knowledge; fine-tuning shapes behaviour.

What it needs: a few hundred to a few thousand good example pairs - quality matters far more than quantity.
What it costs: LoRA is efficient - a single GPU, hours-to-days, not weeks of full retraining.
It stays private: training runs on your hardware; your data never leaves, and the resulting model is yours.
VYROX handles it end-to-end - curate examples, train, validate against real cases, and roll back if it doesn't beat the RAG baseline. We only fine-tune when it earns its keep.

The runtime & agent stack

What you actually drive the model with.

EASIEST · GUI

LM Studio

The most polished, beginner-friendly graphical app. Browse, download and chat with models - no command line.

POPULAR · CLI

Ollama

Developer favourite. One command - ollama run qwen3 - pulls and serves a model in the background.

API · OFFLINE

LocalAI

Drop-in replacement for the OpenAI API. Point existing apps at your local server - text, audio and image, fully offline.

PROD · THROUGHPUT

vLLM

High-throughput serving for teams: tensor-parallel multi-GPU, batching, OpenAI-compatible endpoints.

Open WebUI · team chat Roo Code · IDE agent Cline · autonomous coding Aider · terminal pair-programmer Continue.dev · IDE copilot llama.cpp · the engine MCP · tool connectors

Which runtime, when?

Runtime	Best for	Interface	Multi-GPU	Scale
Ollama	Easiest one-dev prototyping, any OS	CLI + API	Weak (1 GPU/req)	Single user
LM Studio	GUI-first desktop for non-CLI users	GUI + server	Limited	Solo → small team
vLLM	Production multi-user serving	Server + API	Strong (TP/PP)	Production team
llama.cpp	Edge / embedded / max format control	CLI + server	Yes (layers)	Single / edge
LocalAI	OpenAI-compatible API gateway over many backends	REST API	Via backend	Team / gateway
Open WebUI	Shared ChatGPT-style team interface	Web GUI	N/A (front-end)	Team chat

Rule of thumb: solo + simplicity → Ollama / LM Studio · edge/embedded → llama.cpp · concurrent production serving → vLLM · one API over mixed backends → LocalAI · team chat UI → Open WebUI on top. VYROX picks and configures the right combination for your build.

How VYROX delivers

From first call to in-production - in 4 to 8 weeks.

A clear, six-phase engagement. Indicative timelines for a typical SME deployment; you get something concrete at every step.

Free Audit 2-5 days

We map use cases, review your data and infrastructure, and assess fit. You get: a written findings summary and an honest local-vs-cloud-vs-hybrid recommendation. No cost, no obligation.

Spec & Quote 3-7 days

We design models, hardware sizing, deployment mode, integrations and security controls. You get: a detailed spec and a fixed-price quote in RM with timeline.

Procure & Build 1-3 weeks

We source and configure hardware (or provision your VPC), install the stack, and build your RAG/data pipeline. You get: a configured, tested system; procurement handled for you.

Install & Integrate 3-7 days

We deploy on your premises (or tenancy) and connect data sources, SSO and existing tools. You get: a live, integrated, security-hardened system.

Tune & Train 1-2 weeks

We tune retrieval and prompts on your real data, run accuracy checks, and train your staff. You get: a validated system, trained users, and docs (EN/BM).

Ongoing Support continuous

Monitoring, updates, model upgrades and a support line. You get: SLA-backed support and a roadmap for new use cases.

Straight answers

"Sounds good - but is local really practical?" Yes.

MYTH

"Local is a downgrade from ChatGPT."

Fact: for everyday work - drafting, summarising, extraction, coding, internal Q&A - Qwen3.6, Kimi K2.6, GLM-5.1 and DeepSeek V4 run at quality very close to the big clouds. We build hybrids that call cloud only when it genuinely wins.

MYTH

"It'll be obsolete in a year."

Fact: new open models ship monthly and are free. Swapping today's model for next year's best is a one-line change on the same hardware. Your rig gets smarter over time, for RM0.

MYTH

"There's no support if it breaks."

Fact: every build ships with remote monitoring, free model upgrades, and a same-business-day SLA. Standard open-source, documented, your IT trained. No black box, no lock-in.

The Break-Even Guarantee

We won't quote a build unless it pays back within 12 months versus your current cloud bill.

If your measured first-year savings don't beat the subscriptions it replaced, we re-tune the system at our cost until they do - and you keep the hardware either way. Every build also includes remote health monitoring, free model & runtime upgrades, and a same-business-day response SLA.

Book my free Local-AI audit →

Jargon-buster

Every term you'll hear, in plain English.

LLM, open-weight model & parameters

LLM - the AI that understands and generates text; the "brain." Open-weight model - one whose internals are published so you can download and run it yourself (Qwen3, Llama 4, Gemma 3) - what makes local deployment possible. Parameters - the model's internal settings, counted in billions (8B, 70B); more generally means more capability and more hardware.

Quantization, VRAM & GGUF

Quantization (Q4/Q8) - compressing a model so it runs on smaller, cheaper hardware with minimal quality loss. GGUF - a common model file format. VRAM - the memory on a GPU; the single biggest factor in which models you can run and how fast (the model must fit in VRAM).

Tokens, context window & tokens/sec

Tokens - the chunks of text models read and write, roughly ¾ of a word each. Context window - how much text the model can consider at once (its short-term memory), in tokens. Tokens/sec - how fast it generates text; higher feels snappier.

KV-cache & MoE (Mixture of Experts)

KV-cache - a speed optimisation that avoids re-reading earlier text on every word; uses VRAM but makes responses much faster. MoE - a design that activates only the relevant "expert" sub-networks per task, giving big-model quality at lower running cost (e.g. Qwen3-30B-A3B uses ~3B active params of 30B).

RAG, embeddings & vector database

RAG - connecting the model to your own documents so it answers from them, with citations. Embeddings - numeric "fingerprints" of text that let a computer find passages by meaning, not keywords. Vector database - the store (pgvector, Qdrant, Chroma) that holds embeddings and finds the most relevant ones fast: the search index for RAG.

Inference, fine-tuning / LoRA & hallucination

Inference - running the model to get an answer (vs training it). Fine-tuning / LoRA - further-training a model on your data to change its style or specialise it; LoRA is the efficient way to do it without retraining the whole model. Hallucination - when a model states something false but confidently; RAG and grounding are the main defences.

MCP, on-prem vs cloud & the runtimes

MCP (Model Context Protocol) - an open standard that lets the LLM securely connect to tools and data sources. On-prem vs cloud - on-prem runs on hardware you control; cloud runs on someone else's servers via an API. Ollama / vLLM - the engines that serve the model (Ollama simple, vLLM high-throughput); Open WebUI - the ChatGPT-style web interface your staff use.

The honest answers

Local LLM questions you're actually wondering.

Is a local LLM really as good as ChatGPT or Claude?

For everyday business work - drafting, summarising, extraction, classification, internal Q&A, coding help and chat - the 2026 open models (Qwen3.6, Kimi K2.6, GLM-5.1, DeepSeek V4) run at quality very close to the big clouds; Kimi K2.6 even leads open agentic coding. For rare frontier-reasoning tasks we build a hybrid that calls cloud Claude or GPT only when it genuinely wins. You keep almost all the quality and stop paying for everything.

What hardware do I actually need?

It scales with model size: a 7-14B model runs on a 16GB GPU or M4 Mac; a 32B needs a 24-32GB GPU (RTX 3090/4090/5090) or M4 Pro; a 70B needs ~48GB (2× 24GB, RTX 6000 Ada, or a 128GB Mac Studio); 120B-class fits a single RTX PRO 6000 96GB, an A100, a 128GB Mac, or a DGX Spark / Strix Halo mini-PC. Use the VRAM calculator above - or let us size it exactly.

What's the cheapest way to start?

A used RTX 3090 (24GB, ~RM4-6k) or a Mac Mini M4 Pro runs Qwen3-Coder 30B and DeepSeek R1 Distill 32B comfortably - a complete Desk AI build is RM9k-13k. For many one-to-three-person teams that pays for itself inside a year versus cloud seats.

What if a better model launches next year - is my hardware wasted?

No. New open models drop monthly and are free. Swapping today's model for next year's best is a one-line change on the same hardware - no new licence, no migration. GPUs stay useful for several model generations and hold strong resale value.

Can a TPU or Coral stick run a local LLM?

No. The Coral Edge TPU is built for tiny vision models and can't run modern LLMs (no DRAM, no transformer support). Cloud TPUs and Groq/Cerebras are rented cloud services, not on-prem hardware you own. For genuine local hosting the practical accelerators are NVIDIA GPUs, AMD Instinct/Radeon, Apple Silicon, and unified-memory mini-PCs.

Who maintains it? What if it breaks at 2am?

We don't install and vanish. Every build ships with remote health monitoring, free model & runtime upgrades, and a same-business-day response SLA. It's standard open-source (Ollama, vLLM, Open WebUI) - fully documented, your IT trained. You own the box; we keep it running.

How much does it cost to run - power, maintenance?

A Desk/Studio build draws roughly the power of a gaming PC - a few ringgit of electricity a day. The one-time price includes hardware, setup, tuning, integration and training; ongoing cost is electricity plus an optional support plan. We put the full picture into the costed audit.

Can it connect to our tools - email, docs, ERP, WhatsApp?

Yes. Over MCP we wire the local model into email, documents, AutoCount/ERP and WhatsApp - the same integrations as cloud AI, with none of the data leaving your network.

Your move

Stop renting your AI. Own it by next quarter.

Book a free 45-minute Local-AI Audit. We measure your current cloud spend, spec the exact build, and give you the costed break-even date - in writing, no obligation.

Book via WhatsApp → Chat with an engineer

No deck pitch. Just engineers sizing your build.

Run AI on your own hardware.

Stop renting it.

Three things the cloud can never fully give you.

100% data privacy

Works fully offline

No monthly subscription

Cloud sends your data out. Local keeps it in.

17 things businesses run on a private local LLM.

Three ways to put your local AI to work.

A private assistant your staff chat with.

A fixed pipeline the AI fills in - predictable and auditable.

Give it a goal. It plans, uses tools, acts, and checks its own work.

AI Doctor. AI Accountant. AI Lawyer. On your own hardware.

A clinical co-pilot that never sends a patient record to the cloud.

Books, tax and reports - drafted in-house, where client data belongs.

Privileged work stays privileged - on your own server.

Make the model answer from YOUR files - privately.

How it works - 6 steps

"Is local as good as ChatGPT?" On the work you do - close enough to matter.

The open models worth running - filter by what you do.

GPUs, Apple Silicon, mini-PCs, servers - the complete map.

NVIDIA & AMD GPUs

Apple Silicon - unified memory advantage

Mini-PCs & "AI-in-a-box"

TPUs & other accelerators - the honest truth

Multi-GPU servers & rack

Pick a model. See the VRAM it needs and what runs it.

Smaller numbers, almost the same brain.

Tell us your hardware. We'll list what fits.

Model size → memory → hardware, at a glance.

The details that decide whether you can actually run it.

Open-weight licensing (commercial use)

Power, heat & electrical

Networking & access

Backup & high availability

Warranty & support

How fast is local, really?

Cloud AI is a bill that never stops. Local is a cost that ends.

Local, cloud, or hybrid? The straight trade-off.

Four ready-to-deploy local-AI builds.

Desk AI

Studio AI

Engine AI

Rack AI

Tell us your purpose. Get your build, cost and payback.

Adjust the model, the hardware and the usage. See the full cost.

We'll size your exact build and put the break-even date in writing.

"Where does my data go?" Nowhere you don't control.

On-premise server

Air-gapped

Private cloud / VPC

Most customers need RAG, not fine-tuning. We'll tell you which.

What you actually drive the model with.

LM Studio

Ollama

LocalAI

vLLM

Which runtime, when?

From first call to in-production - in 4 to 8 weeks.

"Sounds good - but is local really practical?" Yes.

We won't quote a build unless it pays back within 12 months versus your current cloud bill.

Every term you'll hear, in plain English.

Local LLM questions you're actually wondering.

Stop renting your AI. Own it by next quarter.