Can an RTX 4070 Ti SUPER run Qwen3 32B?

Barely

It only loads at IQ3_XXS, which compresses the weights far enough to visibly degrade the model, so we would not call that running it.

In practice

It loads, and we would not call that running it. The only quantization that fits the GeForce RTX 4070 Ti SUPER is IQ3_XXS, which compresses the weights far enough that Qwen3 32B loses a visible amount of its ability, so we do not recommend it. At Q4_K_M it needs 20.42 GB against the 15.2 GB available.

Running it properly means a split with system RAM: at Q4_K_M about 47 of 64 layers fit on the card, and the estimate falls to 4.8 tokens per second. A smaller model at a proper quantization is a better answer than a larger one squeezed in, and the next section lists the ones that fit.

The numbers

Qwen3 32B has 32.76 billion parameters across 64 layers. The GeForce RTX 4070 Ti SUPER has 16 GB of VRAM at 672 GB/s, of which about 15.2 GB is left for a model once the operating system has taken its share.

QuantizationTotal neededFitsSpeed
Q8_0 34.09 GB partial 1.5 t/s
Q6_K 26.86 GB partial 2.3 t/s
Q5_K_M 23.63 GB partial 3.1 t/s
Q4_K_M 20.42 GB partial 4.8 t/s
IQ4_XS 18.26 GB partial 7.1 t/s
Q3_K_M 17 GB partial 11.1 t/s
IQ3_XXS degraded 13.83 GB yes 36.9 t/s
Q2_K degraded 14.91 GB yes 34.2 t/s
IQ2_XXS degraded 10.11 GB yes 51.1 t/s

3 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.

What to run instead

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

Shortening the context will not rescue this one: at 4k it still wants 19.42 GB against 20.42 GB at 8k, because the weights, not the cache, are what fill the card.

Models of this size are what a second GPU or a large-memory machine is for. The multi-GPU guide shows what 2 cards add, and the Mac and PC comparison shows where unified memory holds a model that no single card can.

Or the hardware that does run it

The cheapest device we track that runs Qwen3 32B at a quantization worth using is the Radeon RX 7900 XT, around $650 , running it at IQ4_XS at about 28.0 tokens per second. Check the current price (affiliate link, indicative price reviewed 2026-09-01). Compare every option.

What else an RTX 4070 Ti SUPER runs well

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

ModelQuantizationMemorySpeed
Qwen3.5 27B IQ4_XS 14.2 GB 35.9 t/s
Gemma 4 12B Instruct Q8_0 13.55 GB 37.6 t/s
GPT OSS 20B MXFP4 11.55 GB 120 t/s
Qwen3 14B Q6_K 12.38 GB 41.4 t/s

The same model on nearby hardware

Memory decides whether Qwen3 32B 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.

DeviceMemoryBandwidthResult
GeForce RTX 4070 12 GB 504 GB/s does not fit
GeForce RTX 5080 16 GB 960 GB/s does not fit
GeForce RTX 4090 24 GB 1008 GB/s Q4_K_M, 37.1 t/s

Questions people ask

How much VRAM does Qwen3 32B need on RTX 4070 Ti SUPER?

At Q4_K_M with an 8k context window it needs 20.42 GB in total: 17.98 GB of weights, 2 GB of KV cache and 0.44 GB of runtime overhead. The GeForce RTX 4070 Ti SUPER makes about 15.2 GB available once the operating system has taken its share.

What should I run instead of Qwen3 32B on RTX 4070 Ti SUPER?

Qwen3 30B A3B fits at Q3_K_M and runs at about 127 tokens per second. Smaller models from the same family are the usual answer.

Keep going

Everything the GeForce RTX 4070 Ti SUPER runs, the full breakdown for Qwen3 32B, how the numbers are made in the methodology, or a different pairing in the calculator.