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Run LLM Locally

QwQ 32B on the RX 7900 XTX

With 24 GB of VRAM, the RX 7900 XTX runs QwQ 32B at Q4_K_M entirely on the GPU with an 8K context (about 22.4 GB needed). At Q8_0 it does not fit in VRAM.

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QwQ 32B

Q4_K_M · 8K context

Compatible

The model needs about 22.4 GB and fits in the 23.3 GB available (0.9 GB left).

Your hardware
RX 7900 XTX 24 GB · 64 GB RAM
Estimated memory
~22.4 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
27–39 tok/s estimate
Largest context in fast memory
11K tokens
Confidence: low

Based on: memory bandwidth (960 GB/s) and model size

Memory breakdown

~22.4 GB (21.7 GB–23.2 GB) needed · estimate

Fits entirely in VRAM · 0.9 GB left

  • Model weights18.5 GB
  • KV cache2 GB
  • Runtime overhead0.9 GB
  • Safety margin1.1 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RX 7900 XTX

QwQ 32B on RX 7900 XTX + 64 GB RAM · 8K context
QwQ 32B Q8_08K~37.1 GB · 1.4–2.5 tok/s estimatePossible but slow
QwQ 32B Q6_K8K~29.3 GB · 3–5.5 tok/s estimateCPU/GPU offload
QwQ 32B Q5_K_M8K~25.8 GB · 6–12 tok/s estimateCPU/GPU offload
QwQ 32B Q4_K_M8K~22.4 GB · 27–39 tok/s estimateCompatible
QwQ 32B IQ4_XS8K~20.3 GB · 29–43 tok/s estimateCompatible
QwQ 32B Q3_K_M8K~18.6 GB · 32–47 tok/s estimateExcellent

Alternatives

GPUs that run QwQ 32B Q4_K_M entirely in VRAM

Other models that fit the RX 7900 XTX

Where to buy a RX 7900 XTX

Prices and links follow the market you choose.

Radeon RX 7900 XTX

24 GB GDDR6 · 960 GB/s · 355 W

CompatibleRuns QwQ 32B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RX 7900 XTX run QwQ 32B?
The model needs about 22.4 GB and fits in the 23.3 GB available (0.9 GB left).
Which quantization should I use on the RX 7900 XTX?
Q4_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~22.4 GB / 23.3 GB.
What is the largest context on the RX 7900 XTX?
At Q4_K_M, about 11K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is QwQ 32B (RX 7900 XTX)?
We estimate 27–39 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.