Yes — with 9.1 GB to spare

Qwen2.5-Coder 7B at Q4_K_M fits your RTX A4000 entirely on the GPU at 8K context, at an estimated 63 tokens per second. There is room for its full 128K window.

Fully on GPU 8K context Q4_K_M · 4.3 GB Apache 2.0 Released Nov 2024

The standard local autocomplete model — small enough to keep resident all day, and still the best FIM model under 8B.

What hardware do I need for Qwen2.5-Coder 7B? →

The VRAM budget

weights 4.3 GB

Weights 4.3 GB KV cache @ 8K 0.44 GB Runtime overhead 0.6 GB Free 9.1 GB of 14.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	14.2 GB	15.2 GB	—	~14	Reference	0.8 GB over
Q8_0	7.5 GB	8.6 GB	114K	36	−0.1% ppl	Long context
Q6_K	5.8 GB	6.9 GB	128K	47	−0.4% ppl	Long context
Q5_K_M	5.0 GB	6.1 GB	128K	54	−0.8% ppl	Long context
Q4_K_M	4.3 GB	5.3 GB	128K	63	−1.9% ppl	Recommended
Q3_K_M	3.5 GB	4.5 GB	128K	78	−5.4% ppl	Long context
Q2_K	3.0 GB	4.0 GB	128K	91	−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

Ollama llama.cpp LM Studio vLLM

terminal

$ ollama pull qwen2.5-coder:7b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run qwen2.5-coder:7b

The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.

01Download is 4.3 GB. Keep it on an SSD — a first load off a spinning disk takes minutes.

02Close anything else holding VRAM. A browser with hardware acceleration can sit on 1–2 GB.

03There is room to go to the model's full 128K context on this card.

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