Yes — with 1.7 GB to spare

Qwen2.5-Coder 7B at Q4_K_M fits your GeForce RTX 4060 entirely on the GPU at 8K context, at an estimated 38 tokens per second. Past 38K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.

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 1.7 GB of 7.0 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	14.2 GB	15.2 GB	—	~3.8	Reference	8.2 GB over
Q8_0	7.5 GB	8.6 GB	—	~13	−0.1% ppl	1.6 GB over
Q6_K	5.8 GB	6.9 GB	10K	28	−0.4% ppl	Fits
Q5_K_M	5.0 GB	6.1 GB	25K	33	−0.8% ppl	Long context
Q4_K_M	4.3 GB	5.3 GB	38K	38	−1.9% ppl	Recommended
Q3_K_M	3.5 GB	4.5 GB	53K	47	−5.4% ppl	Long context
Q2_K	3.0 GB	4.0 GB	62K	55	−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 38K context on this card.

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