Qwen3 30B A3B · 10 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for Qwen3 30B A3B?

Qwen3 30B-A3B is the model that changes the local-LLM math. It's a mixture-of-experts design, 30B parameters in memory, only ~3B active per token, so it needs the VRAM of a big model but generates at small-model speed: 309 tok/s on the RTX PRO 6000 Blackwell in our tests, faster than the dense 8B on the same silicon. Measured on 10 GPUs, ~20GB peak VRAM at Q4_K_M.

Benchmarked weights: bartowski/Qwen_Qwen3-30B-A3B-GGUF

This is where it gets good. In my tiering of the Qwen lineup, this is the model where local LLM work stops being a compromise. The MoE trick means you pay for capability in VRAM instead of speed, 20GB in memory, but per-token compute like a 3B, and the result is a strong 30B-class model that generates at 300+ tok/s on the right card. If you own a 24GB card, this should probably be your default daily model. It's the reason I tell people a used 24GB card is the smartest local-AI purchase: this model is what it unlocks.

What the measurements show. The RTX PRO 6000 Blackwell tops the chart at 309 tok/s. And does it at 132W, 2.34 tok/W, because the active-parameter count is so small the card never works hard. The H200 follows at 292 tok/s. Even the A10G, a modest cloud card, holds 150 tok/s. Compare that to the dense Qwen3 32B, where the fastest result we have anywhere is 84 tok/s: same memory class, nearly 4× the generation speed. The ~20GB floor means 24GB consumer cards, a $999 RX 7900 XTX, a used RTX 3090, clear it with room for context.

Our verdict

Qwen3 30B-A3B: 309 tok/s peak at just 132W, ~20GB floor. The MoE architecture makes it generate like a small model while reasoning like a big one, in our data it's nearly 4× faster than the dense 32B in the same memory class. If you have a 24GB card, this is the model that justifies it.

FAQ

Why is a 30B model this fast?

It's a mixture-of-experts: all 30B parameters sit in VRAM, but only ~3B activate per token. You pay the memory cost of a big model and the compute cost of a small one. Which is why it hit 309 tok/s where the dense Qwen3 32B tops out at 84 in our database.

What GPU do I need for Qwen3 30B-A3B?

~20GB measured peak at Q4_K_M, so a 24GB card: RX 7900 XTX ($999 MSRP), used RTX 3090, RTX 4090. 16GB cards don't fit it at this quant.

Qwen3 30B-A3B or Qwen3 32B dense?

For most local use, the MoE. It's nearly 4× faster in our measurements (309 vs 84 tok/s at each chart's top) in the same VRAM class. The dense 32B only earns its slot when its per-token depth genuinely wins on your tasks.

How efficient is it?

The best big-model efficiency we've measured outside coder-30B: 2.34 tok/W on the RTX PRO 6000 (309 tok/s at 132W). The low active-parameter count keeps the silicon loafing.

Is this the best model for a used RTX 3090?

It's the strongest argument for one. The 3090's 24GB clears the ~20GB floor, and the MoE speed means the aging bandwidth still delivers a fast experience, a big-model brain on a 2020 card.