Can the RTX 5090 Run Qwen3 30B-A3B?
Runs
Yes. Qwen3 30B-A3B at Q4_K_M is a 18.6 GB download (Hugging Face file listing for unsloth/Qwen3-30B-A3B-GGUF), and the RTX 5090 has 32 GB of VRAM, leaving about 13.4 GB for the KV cache and runtime. One published run puts generation at 234 tokens per second; a single cited measurement, not a median. Cited run: Hardware Corner.
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Published runs for Qwen3 30B-A3B on the RTX 5090
| Generation | Quant | Runtime | Context | Source | Date |
|---|---|---|---|---|---|
| 234 tok/s | q4_K_M | llama.cpp | 4K | Hardware Cornerlabelled "qwen3moe:30b-a3b" | 2025-06-01 |
More about this pair
How much VRAM does Qwen3 30B-A3B need?
The Q4_K_M file is 18.6 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 20.1 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What speed does Qwen3 30B-A3B reach on the RTX 5090?
The fastest published run is 234 tokens per second (Hardware Corner, q4_K_M, llama.cpp). Qwen3 30B-A3B is a mixture-of-experts model that reads about 2.01 GB of active weights per token, so the RTX 5090's 1792 GB/s of memory bandwidth puts the theoretical ceiling near 892 tokens per second. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it, and SpecPicks rejects any published figure above it.
Check another pair
Qwen3 30B-A3B on other GPUs
- RTX 4060 — Offload
- RTX 3060 Ti — Offload
- RTX 3060 12GB — Offload
- Intel Arc B580 — Offload
- RTX 4070 SUPER — Offload
- RTX 4060 Ti 16GB — Offload
- RTX 5060 Ti 16GB — Offload
- Intel Arc A770 16GB — Offload
- Radeon RX 9070 XT — Offload
- RTX 4070 Ti SUPER — Offload
- RTX 4080 — Offload
- RTX 5070 Ti — Offload
- RTX 5080 — Offload
- Tesla P40 24GB — Runs
- RTX 3090 — Runs
- Radeon RX 7900 XTX — Runs
- RTX 4090 — Runs, 196 tok/s (1 run)
- RTX A6000 48GB — Runs
- RTX PRO 5000 Blackwell 48GB — Runs, 189 tok/s (1 run)
- Instinct MI210 64GB — Runs
- RTX PRO 6000 Blackwell 96GB — Runs
Other models on the RTX 5090
- Llama 3.2 1B — Runs
- Llama 3.2 3B — Runs
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs, 180 tok/s median
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs, 186 tok/s (1 run)
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 89.9 tok/s (1 run)
- DeepSeek-R1-Distill-Qwen 14B — Runs, 89.1 tok/s (1 run)
- Qwen3 14B — Runs, 124 tok/s (1 run)
- Phi-4 14B — Runs, 130 tok/s (1 run)
- gpt-oss-20b — Runs, 298 tok/s (1 run)
- Gemma 3 27B — Runs
- Gemma 2 27B — Runs
- Qwen3 32B — Runs, 58 tok/s median
- Qwen2.5 32B — Runs, 54 tok/s median
- DeepSeek-R1-Distill-Qwen 32B — Runs, 54 tok/s median
- Qwen3 32B (Q6_K) — Runs
- Qwen2.5 32B (Q6_K) — Runs
- DeepSeek-R1-Distill-Qwen 32B (Q6_K) — Runs
- Gemma 3 27B (Q8_0) — Runs
- Llama 3.1 70B — Offload
- Llama 3.3 70B — Offload
- DeepSeek-R1-Distill-Llama 70B — Offload
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling (memory bandwidth ÷ GB read per token: the whole file for a dense model, only the active experts' weights for a mixture-of-experts model such as gpt-oss-20b or Qwen3 30B-A3B, which is why those can post figures above bandwidth ÷ file size), it is not slower than the same card on a model twice its size, a single run is not slower than the same card on a larger dense model, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
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This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.