Can an RTX 4070 run Qwen3 30B A3B?

In practice

Qwen3 30B A3B does not fit in 12 GB of VRAM on its own, and it does not have to. It is a mixture-of-experts model, so most of its weight is in experts of which only a few are read per token. Keeping the experts in system RAM and everything dense on the card is much faster than moving whole layers, and the engine puts about 58% of the experts on the GPU. The estimate is about 42.1 tokens per second.

Plan on at least 8 GB of free system RAM beyond what the card holds, and know that the speed is set mostly by how fast that RAM is. We have not measured a mixture-of-experts model on our own machine, so read the figure as good to about 30%. The command below is the llama.cpp way to set it up.

The numbers

Qwen3 30B A3B has 30.53 billion parameters across 48 layers, of which 3.34 billion are read for each token. The GeForce RTX 4070 has 12 GB of VRAM at 504 GB/s, of which about 11.2 GB is left for a model once the operating system has taken its share.

1 of the 9 levels fit entirely. Levels marked degraded are listed for completeness, not as advice. Rows marked partial or experts in RAM run with part of the model in system RAM, at the speed shown.

The settings we would use

llama-server -m <model>.gguf -ngl all --n-cpu-moe 20 -c 8192 -fa on

The first 20 of 48 layers keep their experts in system RAM, and everything dense stays on the GPU. Lower the number until the load fails, then go back one.

This split is set in llama.cpp. Ollama chooses its own placement and does not take these options, so run ollama ps to see what it picked. The llama.cpp flags guide explains each option above.

If you want it faster

A smaller model from the same family is the straightforward answer. These fit on an RTX 4070 at a quantization worth using:

• Qwen3 14B, 14.77B at Q5_K_M, 10.96 GB, about 35.3 tokens/s
• Qwen2.5 14B Instruct, 14.77B at Q4_K_M, 9.81 GB, about 39.6 tokens/s
• Qwen2.5 Coder 14B Instruct, 14.77B at Q4_K_M, 9.81 GB, about 39.6 tokens/s

Shortening the context will not rescue this one: at 4k it still wants 17.71 GB against 18.08 GB at 8k, because the weights, not the cache, are what fill the card.

Or the hardware that does run it

The cheapest device we track that runs Qwen3 30B A3B at a quantization worth using is the GeForce GTX 1650, around $100 used , running it at Q4_K_M at about 17.0 tokens per second. Check the current price (affiliate link, indicative price reviewed 2026-09-01). Compare every option.

What else an RTX 4070 runs well

For comparison, these are the other popular models that fit this device at a sound quantization, best pairings first.

The same model on nearby hardware

Memory decides whether Qwen3 30B A3B loads and bandwidth decides how quickly it answers. Here is what a step down, a card with the same memory and a step up do with it.

Wait for the first word350 ms

Then writes at42.1 tok/s

Whole answer4.5 s

YouWhy does my model use more memory when the conversation gets longer?

Model

Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.

Questions people ask

Keep going

Everything the GeForce RTX 4070 runs, the full breakdown for Qwen3 30B A3B, how the numbers are made in the methodology, or a different pairing in the calculator.