Yes — with 27.9 GB to spare

Qwen3 Coder 30B-A3B at Q4_K_M fits your L40S entirely on the GPU at 8K context, at an estimated 108 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.

What hardware do I need for Qwen3 Coder 30B-A3B? →

The VRAM budget

weights 17.1 GB

Weights 17.1 GB KV cache @ 8K 0.75 GB Runtime overhead 0.6 GB Free 27.9 GB of 46.4 GB

Quantisation ladder

Quant	Weights	Total @ 8K	Max context	Tok/s	Quality	Fit
Q8_0	30.2 GB	31.5 GB	166K	62	−0.1% ppl	Long context
Q6_K	23.3 GB	24.6 GB	240K	80	−0.4% ppl	Long context
Q5_K_M	20.1 GB	21.5 GB	256K	92	−0.8% ppl	Long context
Q4_K_M	17.1 GB	18.5 GB	256K	108	−1.9% ppl	Recommended
Q3_K_M	13.9 GB	15.2 GB	256K	134	−5.4% ppl	Long context
Q2_K	11.9 GB	13.2 GB	256K	156	−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-coder:30b
$ OLLAMA_CONTEXT_LENGTH=8192 \
    ollama run qwen3-coder:30b

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

01Download is 17.1 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 256K context on this card.

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