Best hardware for a local coding LLM
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The best hardware for a local coding LLM holds a 32B-class or mixture-of-experts model in VRAM with room for long context, and writes fast enough for agents that make many calls. A 24 GB CUDA machine covers that. Larger coding models need unified memory.
Olares One → Local LLM hardware → VRAM · quantization · tokens per second → The right machine for your model size
What does the best hardware for a local coding LLM need?
The best hardware for a local coding LLM holds a 32B-class or mixture-of-experts model in VRAM, leaves room for long context and reads long prompts quickly. Coding tools paste whole files, diffs and logs into each prompt, and agents chain many calls, so prefill speed and generation speed both count for more than they do in chat.
Code also breaks on small mistakes. Heavy quantization and very small models cost more here than in casual writing.
Why does context cost so much memory in coding?
Context costs memory because every token of it sits in the KV cache on the GPU, and coding sessions carry a lot of context. On a 24 GB card, a 32B model at 4-bit takes roughly 19-20 GB by our approximate rule. That leaves only a few GB for open files, diffs and history.
A mixture-of-experts model like Qwen3-30B-A3B needs similar memory, since all experts stay loaded, but it generates much faster. On 24 GB you pick between a 32B-class model with moderate context and a smaller model with long context. A 32 GB card or a unified-memory machine gives more room. See how much VRAM you need and quantization explained.
How fast is fast enough for agents and teams?
For a coding team, per-request speed under load matters more than peak speed. We'd choose a fast mixture-of-experts model on vLLM over a slower, larger dense model. In Olares' own test on Olares One, Qwen3-30B-A3B held about 81 tokens per second per request with 8 concurrent requests on vLLM, against 157 with one request.
Ollama in the same test dropped to 32 tokens per second per request at 8 concurrent. Editor plugins and agent tools that speak an OpenAI-compatible API can point at a vLLM server without code changes. Our view would change if your team's tasks clearly need a 70B-class model. Then pay for unified memory and accept slower tokens. More in tokens per second explained.
Which machines suit local coding?
A 24 GB CUDA machine suits 32B-class coding models. A 32 GB RTX 5090 desktop build adds context headroom. Unified-memory machines suit 70B and larger coding models at a lower speed per token. Olares One sits in the first group at 4,999 USD, beside a used RTX 3090 tower that one build guide estimates at about 3,800 USD.
The RTX 5090 card costs 1,999 USD before the PC around it and draws up to 575 W, NVIDIA lists. The DGX Spark, Mac Studio and Ryzen AI Max+ 395 boxes offer 64 GB to 512 GB across their configurations. For a coding server shared by a team, read hardware for an inference server.
Questions & answers
Can a local coding model replace a cloud assistant?
That depends on the model and the task. Test the model you can run on real tickets from your own codebase before buying hardware for it.
Does a coding LLM need CUDA?
Not strictly. llama.cpp runs on Macs and AMD. But vLLM, which serves several developers well, and many fine-tuning tools are built for CUDA first.
Sources for this entry
- Olares blog: local AI hardware benchmarking (Oct 2025) (Company-published)
- Olares One FAQ (Olares docs) (Company-published)
- Olares One: maker's page (Maker-published)
- Used RTX 3090 tower build: maker's page (Maker-published)
- NVIDIA GeForce RTX 5090 (desktop card): maker's page (Maker-published)
- NVIDIA DGX Spark: maker's page (Maker-published)
- Apple Mac Studio (M5 Ultra): maker's page (Maker-published)
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Cite this entry
Best Local LLM Hardware. “Best hardware for a local coding LLM.” Content reviewed 13 November 2026.
https://bestlocalllmhardware.com/local-llm-hardware-for-coding/