Local AI Workstation Singapore
Custom-built PCs for running LLaMA, Mistral, Ollama, and other AI models offline. No cloud subscription, full privacy, full speed. VRAM determines everything. Starting from $600.
Run AI Models Offline — No API Costs, No Data Leaving Your Machine
Local AI lets you run large language models like LLaMA 3, Mistral, and Phi-3 entirely on your own hardware. No monthly subscription, no data sent to external servers, no rate limits. You own the model and the conversation. VRAM is the single most critical spec — the model must fit in GPU VRAM for fast token generation (30–50 tok/sec). When it overflows to RAM, speed drops to 1–3 tok/sec.
Model must fit in VRAM for GPU inference. RTX 3060 12GB handles 7B models fully in VRAM at 30–50 tokens/sec.
When the model exceeds VRAM, layers spill to RAM. 32 GB RAM allows partial GPU offloading of 13B+ models at reduced speed.
LLM model files range from 4–8 GB each at Q4 quantisation. A small model library needs 50–100 GB. NVMe SSD recommended for fast model loading.
Which Models Can Each Build Run?
| Build | VRAM | Models (Q4 quant) | Speed |
|---|---|---|---|
| RTX 3060 Build | 12 GB | LLaMA 3 8B, Mistral 7B, Phi-3, Gemma 7B | 30–50 tok/s |
| RTX 3070 Build | 8 GB | LLaMA 3 8B (Q4), Mistral 7B, Phi-3 | 25–40 tok/s |
| RTX 3080 Build | 10 GB | LLaMA 3 8B, Mistral 7B, small 13B models | 35–50 tok/s |
ⓘ For 70B+ models, you need 40+ GB VRAM — beyond our standard builds. WhatsApp for a custom high-VRAM configuration.
Local AI Desktop Build Tiers
ⓘ Prices are estimated builds — WhatsApp for exact quote.
All builds use refurbished components tested in-house • Windows 11 available on request • RAM upgradeable to 32 GB • Prices exclude monitor, keyboard & mouse
Frequently Asked Questions
What software do I need to run local AI models?
Ollama is the easiest starting point — one command installs and runs models like LLaMA 3 or Mistral. LM Studio provides a GUI with a built-in chat interface. Both are free and work on Windows with NVIDIA GPUs. For developers, llama.cpp also runs directly on the command line.
Why is the RTX 3060 12GB recommended over the RTX 3070 8GB for local AI?
For LLM inference, VRAM capacity matters more than raw GPU performance. The RTX 3060 12GB has 4 GB more VRAM than the RTX 3070 8GB. This means the RTX 3060 fits larger 7B models fully in VRAM without needing to split layers across GPU and RAM, resulting in faster and more consistent output speed.
What is the difference between GPU inference and CPU inference?
When a model runs on GPU (VRAM), inference runs at 30–50 tokens per second. When it runs on CPU (system RAM), speed drops to 1–5 tokens per second — a conversation becomes painfully slow. The goal is to keep as much of the model in VRAM as possible.
Can I run local AI and Stable Diffusion on the same build?
Yes — but not simultaneously (both need VRAM). The RTX 3060 12GB is the most versatile build: it fits LLaMA 3 8B in VRAM for chat, and also handles SDXL image generation. Switch between them by closing one application before opening the other.
Is my data private when running local AI?
Yes — completely. When running Ollama or LM Studio locally, no data leaves your machine. All inference happens on your GPU. This is one of the main reasons professionals and businesses choose local AI over cloud APIs like ChatGPT or Claude.
Get a Local AI Workstation Quote
Tell us which models you want to run and your budget — we’ll spec the right build.
