Can RTX 3090 24GB run Qwen3-Coder 30B-A3B?
Measured results for Qwen3-Coder 30B-A3B (30.53B MoE, 3.3B active) on RTX 3090 24GB, plus what fits and the commands to run it.
Short answer
Yes. Qwen3-Coder 30B-A3B Q4_K_M fits fully on RTX 3090 24GB at 16k context, using 20.2 of 23.0 GB usable, and generates about 206.3 tokens/s in our own run. For Q4_K_M, our bandwidth-only estimate was ~257 tok/s against a measured median of ~206 (24% too optimistic).
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 206.3 | 206.3–206.3 | 4305 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 206.3 | 3 repeats | 4304 | 3 | own measurement |
Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench b11658 on a rented GPU.
Speed vs context depth measured
Our own llama-bench runs on GeForce RTX 3090 (flash attention on, 3 repeats): generation and prompt speed with the given number of tokens already in the context.
| Quant | Context depth | Generation tok/s | vs depth 0 | Prompt tok/s |
|---|---|---|---|---|
| Q4_K_M | 0 | 206.3 | 100% | 4304 |
| Q4_K_M | 4k | 188.6 | 91% | 3710 |
| Q4_K_M | 16k | 149.4 | 72% | 2381 |
Memory and context limits
Q4_K_M weights are 18.6 GB; the 16k f16 KV cache adds 1.61 GB. Largest context that fits in 23.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 32k |
| q8_0 | 64k |
| q4_0 | 128k |
Native limit 256k, 1M with YaRN. Full KV cache table for Qwen3-Coder 30B-A3B.
Copy-paste commands
The advisor's pick for this hardware: Q4_K_M, 16k context.
llama-server -hf unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M
FAQ
How fast is Qwen3-Coder 30B-A3B on RTX 3090 24GB?
About 206.3 tokens/s for generation (Q4_K_M on llama.cpp CUDA, 128-token generation), 4304 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Qwen3-Coder 30B-A3B need on RTX 3090 24GB?
The advisor's pick, Q4_K_M, is 18.6 GB of weights plus 1.61 GB of KV cache at 16k context (20.2 GB), against 23.0 GB usable.
What is the maximum context for Qwen3-Coder 30B-A3B on RTX 3090 24GB?
With an f16 KV cache about 32k tokens, 64k with q8_0 and 128k with q4_0, bounded by the model's own limit.
Related
All models on RTX 3090 24GB · Qwen3-Coder 30B-A3B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 3090 24GB vs RTX 4090 24GB · RTX 3090 24GB vs RTX 5090 32GB · RTX 3090 24GB vs AMD RX 7900 XTX 24GB