Yes — with 186.3 GB to spare
Qwen3 Coder 30B-A3B at Q4_K_M fits your CPU only · DDR5 8-channel server entirely on the GPU at 8K context, at an estimated 31 tokens per second. There is room for its full 256K window.
Fully on GPU
8K context
Q4_K_M · 17.1 GB
Apache 2.0
Released Jul 2025
Agentic coding MoE with a 256K native window. Still the most-downloaded local code model.
The VRAM budget
weights 17.1 GB
Weights 17.1 GB
KV cache @ 8K 0.75 GB
Runtime overhead 0.6 GB
Free 186.3 GB of 204.8 GB
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 30.2 GB | 31.5 GB | 256K | 18 | −0.1% ppl | Long context |
| Q6_K | 23.3 GB | 24.6 GB | 256K | 23 | −0.4% ppl | Long context |
| Q5_K_M | 20.1 GB | 21.5 GB | 256K | 26 | −0.8% ppl | Long context |
| Q4_K_M | 17.1 GB | 18.5 GB | 256K | 31 | −1.9% ppl | Recommended |
| Q3_K_M | 13.9 GB | 15.2 GB | 256K | 38 | −5.4% ppl | Long context |
| Q2_K | 11.9 GB | 13.2 GB | 256K | 44 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it.
How to run it
$ llama-server \
-hf Qwen/Qwen3-Coder-30B-A3B-Instruct:Q4_K_M \
-c 8192 -ngl 99
The engine underneath most of the others. Every knob is exposed. More on llama.cpp.
01Download is 17.1 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.
02With no GPU, thread count matters more than clock. Start at one thread per physical core.
03There is room to go to the model's full 256K context on this card.