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Can the Radeon RX 9070 XT Run DeepSeek-R1-Distill-Qwen 32B?

Offload

Not entirely. DeepSeek-R1-Distill-Qwen 32B at Q4_K_M is a 19.9 GB download (Hugging Face file listing for bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF), about 3.9 GB more than the Radeon RX 9070 XT's 16 GB, so the remainder has to be offloaded to system RAM. Generation speed is then set by system-memory and PCIe bandwidth rather than by the GPU, so no tokens-per-second figure is quoted for this pair. The smallest card in this matrix that holds it entirely is the Tesla P40 24GB (24 GB).

Model file19.9 GBQ4_K_M
VRAM16 GBRadeon RX 9070 XT
Headroom3.9 GB shortafter weights
Median speed—0 cited runs
Bandwidth ceiling~32 tok/s645 GB/s ÷ 19.85 GB

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Published runs for DeepSeek-R1-Distill-Qwen 32B on the Radeon RX 9070 XT

No published run for this pair has passed the checks yet.

More about this pair

How much VRAM does DeepSeek-R1-Distill-Qwen 32B need?

The Q4_K_M file is 19.9 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 21.4 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.

What speed does DeepSeek-R1-Distill-Qwen 32B reach on the Radeon RX 9070 XT?

No published run of DeepSeek-R1-Distill-Qwen 32B on the Radeon RX 9070 XT has been cited yet. Generation reads every weight once per token, so the Radeon RX 9070 XT's 645 GB/s of memory bandwidth divided by the 19.85 GB file puts the theoretical ceiling near 32 tokens per second once the whole model is resident. That is an upper bound, not a speed estimate: kernel, launch and compute overhead keep real runs below it.

Check another pair

See the full matrix → · All Radeon RX 9070 XT benchmarks →

DeepSeek-R1-Distill-Qwen 32B on other GPUs

Other models on the Radeon RX 9070 XT

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.