Yes — with 4.2 GB to spare

Qwen3 14B at Q4_K_M fits your Radeon RX 7800 XT entirely on the GPU at 8K context, at an estimated 45 tokens per second. Past 35K 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 · 8.3 GB Apache 2.0 Released Apr 2025

The largest Qwen3 that fits a 12 GB card at Q4 with room for context.

What hardware do I need for Qwen3 14B? →

The VRAM budget

weights 8.3 GB

Weights 8.3 GB KV cache @ 8K 1.25 GB Runtime overhead 0.6 GB Free 4.2 GB of 14.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
F16	27.6 GB	29.4 GB	—	~2.2	Reference	15.0 GB over
Q8_0	14.6 GB	16.5 GB	—	~11	−0.1% ppl	2.1 GB over
Q6_K	11.3 GB	13.2 GB	15K	33	−0.4% ppl	Fits
Q5_K_M	9.8 GB	11.6 GB	25K	39	−0.8% ppl	Long context
Q4_K_M	8.3 GB	10.2 GB	35K	45	−1.9% ppl	Recommended
Q3_K_M	6.7 GB	8.6 GB	45K	56	−5.4% ppl	Long context
Q2_K	5.8 GB	7.6 GB	51K	65	−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 qwen3:14b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run qwen3:14b

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

01Download is 8.3 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 35K context on this card.

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