Skip to content
Run LLM Locally

Qwen2.5 32B Instruct on the RX 7900 GRE

Qwen2.5 32B Instruct at Q4_K_M needs about 22.4 GB, more than the 16 GB of the RX 7900 GRE: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

Qwen2.5 32B Instruct

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 22.4 GB but only 15.5 GB of VRAM is usable: 6.9 GB must be loaded into system RAM. It runs, 69% on the GPU, but more slowly.

Your hardware
RX 7900 GRE 16 GB · 64 GB RAM
Estimated memory
~22.4 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (6.9 GB)
Generation speed
2.5–5 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (576 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

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

6.9 GB must be offloaded to system RAM

  • 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
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)

Other quantizations on the RX 7900 GRE

Qwen2.5 32B Instruct on RX 7900 GRE + 64 GB RAM · 8K context
Qwen2.5 32B Instruct Q8_08K~37.1 GB · 0.9–1.7 tok/s estimateNot recommended
Qwen2.5 32B Instruct Q6_K8K~29.3 GB · 1.4–2.5 tok/s estimatePossible but slow
Qwen2.5 32B Instruct Q5_K_M8K~25.8 GB · 1.8–3.5 tok/s estimatePossible but slow
Qwen2.5 32B Instruct Q4_K_M8K~22.4 GB · 2.5–5 tok/s estimateCPU/GPU offload
Qwen2.5 32B Instruct IQ4_XS8K~20.3 GB · 3.5–7 tok/s estimateCPU/GPU offload
Qwen2.5 32B Instruct Q3_K_M8K~18.6 GB · 5–10 tok/s estimateCPU/GPU offload

Alternatives

Hardware that runs Qwen2.5 32B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XTX

AMD

CompatibleRuns Qwen2.5 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns Qwen2.5 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Qwen2.5 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RX 7900 GRE run Qwen2.5 32B Instruct?
The model needs about 22.4 GB but only 15.5 GB of VRAM is usable: 6.9 GB must be loaded into system RAM. It runs, 69% on the GPU, but more slowly.
Which quantization should I use on the RX 7900 GRE?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RX 7900 GRE?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Qwen2.5 32B Instruct (RX 7900 GRE)?
We estimate 2.5–5 tok/s (estimate, confidence low). Based on: memory bandwidth (576 GB/s) and model size, system RAM bandwidth (~51 GB/s).