HomeCan I run it?Qwen3-30B-A3BQwen3-30B-A3B on an RX 7900 XTX
Yes, but it is tight. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Radeon RX 7900 XTX has 24.0 GB usable at a ~4k context.
Can I run Qwen3-30B-A3B on an RX 7900 XTX?
Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Radeon RX 7900 XTX 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 960 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, 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 449 estimate
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 449 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 | 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 |
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 Qwen3-30B-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 RX 7900 XTX
- Qwen3-32B on an RX 7900 XTXTight
- Qwen3.8-27B on an RX 7900 XTXFits
- Gemma 3 27B on an RX 7900 XTXFits
- Gemma 4 31B on an RX 7900 XTXTight
- gpt-oss-20b on an RX 7900 XTXFits
- Mistral Small 24B on an RX 7900 XTXFits
Quick answers
- Can I run Qwen3-30B-A3B on an RX 7900 XTX?
- Yes, but it is tight. Qwen3-30B-A3B at Q4_K_M needs about 22.3 GB, and the Radeon RX 7900 XTX has 24.0 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 RX 7900 XTX?
- Our estimate is up to 449 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.