HomeCan I run it?Qwen3-30B-A3BQwen3-30B-A3B on an RTX 5090
Yes. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 5090 has 32.0 GB usable at a ~4k context.
Can I run Qwen3-30B-A3B on an RTX 5090?
Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 5090 has 32.0 GB usable. That figure is 32 GB of VRAM on the spec sheet, of which 32.0 GB is usable for weights and cache, at 1792 GB/s memory bandwidth. A Qwen3 Mixture-of-Experts with 30B total weights and about 3B active per token, so it needs 30B-class memory but can decode closer to a small dense model. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- Q4_K_M Fits with headroom
- Memory needed
- 22.3 GB at Q4_K_M, ~4k context
- Usable VRAM
- 32.0 GB of 32 GB VRAM
- Est. tokens/sec
- up to 837 estimate
Memory at each quant
| Quant | Need vs 32.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 22.3 GB | Q4Fits |
| Q8_0 | 38.3 GB | Q8Won’t fit |
| FP16 | 71.2 GB | FP16Won’t fit |
Estimated tokens per second
Estimate: up to 837 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 64k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 0.4 GB | 22.3 GB | Fits |
| 8k | 0.8 GB | 22.7 GB | Fits |
| 16k | 1.6 GB | 23.5 GB | Fits |
| 32k | 3.2 GB | 25.1 GB | Fits |
| 64k | 6.4 GB | 28.3 GB | Fits |
| 128k | 12.9 GB | 34.8 GB | Won’t fit |
What to do instead
- 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 Qwen3-30B-A3B model page.
Other hardware for Qwen3-30B-A3B
- Qwen3-30B-A3B on an RTX 3060 12GBWon’t fit
- Qwen3-30B-A3B on an RTX 3070 TiWon’t fit
- Qwen3-30B-A3B on an RTX 3080Won’t fit
- Qwen3-30B-A3B on an RTX 3090Tight
- Qwen3-30B-A3B on an RTX 4060 Ti 16GBWon’t fit
- Qwen3-30B-A3B on an RTX 4070 Ti SuperWon’t fit
Other models on an RTX 5090
- Qwen3-32B on an RTX 5090Fits
- Qwen3.8-27B on an RTX 5090Fits
- Gemma 3 27B on an RTX 5090Fits
- Gemma 4 31B on an RTX 5090Fits
- gpt-oss-20b on an RTX 5090Fits
- Mistral Small 24B on an RTX 5090Tight
Quick answers
- Can I run Qwen3-30B-A3B on an RTX 5090?
- Yes. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the GeForce RTX 5090 has 32.0 GB usable.
- How much VRAM does Qwen3-30B-A3B 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 Qwen3-30B-A3B on an RTX 5090?
- Our estimate is up to 837 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 64k tokens before the cache pushes the total over usable memory.