How much VRAM does Qwen3-Coder 30B need?

About 22.3 GB at Q4_K_M, 38.3 GB at Q8_0 and 71.2 GB at FP16, estimated for a ~4k context.

How fast is Qwen3-Coder 30B on an RTX 3090?

Our estimate is up to 437 tokens per second at Q4_K_M, as an upper bound for this MoE. It is not a measured benchmark.

What context length can I use?

At Q4_K_M, our KV estimate supports up to about 16k tokens before the cache pushes the total over usable memory.

Home Can I run it?Qwen3-Coder 30BQwen3-Coder 30B on an RTX 3090

Yes, but it is tight. Qwen3-Coder 30B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 3090 has 24.0 GB usable at a ~4k context.

Can I run Qwen3-Coder 30B on an RTX 3090?

Qwen3-Coder 30B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 3090 has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 936 GB/s memory bandwidth. The coding-tuned sibling of Qwen3-30B-A3B. Memory need matches the base 30B-A3B; the weights are trained for code. These are estimates from our formula, not measured benchmarks.

Quant that fits: Q4_K_M Fits, short context only
Memory needed: 22.3 GB at Q4_K_M, ~4k context
Usable VRAM: 24.0 GB of 24 GB VRAM
Est. tokens/sec: up to 437 estimate

Qwen3-Coder-30B-A3B-Instruct · GeForce RTX 3090 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Quant	Need vs 24.0 GB	Verdict
Q4_K_M	22.3 GB	Q4Tight
Q8_0	38.3 GB	Q8Won’t fit
FP16	71.2 GB	FP16Won’t fit

Estimated tokens per second

Estimate: up to 437 tokens/sec at Q4_K_M (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At Q4_K_M, this machine has headroom out to about 16k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

Context headroom for Qwen3-Coder 30B on an RTX 3090 at Q4_K_M
Context	KV cache (FP16)	Total at Q4_K_M	Verdict
4k	0.4 GB	22.3 GB	Tight
8k	0.8 GB	22.7 GB	Tight
16k	1.6 GB	23.5 GB	Tight
32k	3.2 GB	25.1 GB	Won’t fit
64k	6.4 GB	28.3 GB	Won’t fit
128k	12.9 GB	34.8 GB	Won’t fit

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

Run Qwen3.8-27B instead (20.4 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
Q8_0 needs 38.3 GB and does not fit here. The Mac mini M5 Pro 64GB has 48.0 GB usable, enough for Q8.
See the best GPU for this model family and the Qwen3-Coder-30B-A3B-Instruct model page.

Other hardware for Qwen3-Coder 30B

Qwen3-Coder 30B on an RTX 4090Tight
Qwen3-Coder 30B on an RTX 5070 TiWon’t fit
Qwen3-Coder 30B on an RTX 3060 12GBWon’t fit
Qwen3-Coder 30B on a Mac mini 32GBTight
Qwen3-Coder 30B on an Arc B580Won’t fit

Other models on an RTX 3090

Quick answers

Can I run Qwen3-Coder 30B on an RTX 3090?: Yes, but it is tight. Qwen3-Coder 30B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 3090 has 24.0 GB usable.
How much VRAM does Qwen3-Coder 30B need?: About 22.3 GB at Q4_K_M, 38.3 GB at Q8_0 and 71.2 GB at FP16, estimated for a ~4k context.
How fast is Qwen3-Coder 30B on an RTX 3090?: Our estimate is up to 437 tokens per second at Q4_K_M, as an upper bound for this MoE. It is not a measured benchmark.
What context length can I use?: At Q4_K_M, our KV estimate supports up to about 16k tokens before the cache pushes the total over usable memory.