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Run LLM Locally

Qwen2.5 Coder 32B Instruct on the Arc A770 16 GB

Qwen2.5 Coder 32B Instruct at Q4_K_M needs about 22.4 GB, more than the 16 GB of the Arc A770 16 GB: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

Qwen2.5 Coder 32B Instruct

Q4_K_M · 8K context

Possible but slow

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.

Estimated speed is at most 4.5 tokens/s: usable, but slow.

Your hardware
Arc A770 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
2.5–4.5 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (560 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 (SYCL) · 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 Arc A770 16 GB

Qwen2.5 Coder 32B Instruct on Arc A770 16 GB + 64 GB RAM · 8K context
Qwen2.5 Coder 32B Instruct Q8_08K~37.1 GB · 0.9–1.7 tok/s estimateNot recommended
Qwen2.5 Coder 32B Instruct Q6_K8K~29.3 GB · 1.3–2.5 tok/s estimatePossible but slow
Qwen2.5 Coder 32B Instruct Q5_K_M8K~25.8 GB · 1.7–3.5 tok/s estimatePossible but slow
Qwen2.5 Coder 32B Instruct Q4_K_M8K~22.4 GB · 2.5–4.5 tok/s estimatePossible but slow
Qwen2.5 Coder 32B Instruct IQ4_XS8K~20.3 GB · 3–6.5 tok/s estimateCPU/GPU offload
Qwen2.5 Coder 32B Instruct Q3_K_M8K~18.6 GB · 4.5–8.5 tok/s estimateCPU/GPU offload

Alternatives

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

Prices and links follow the market you choose.

Radeon RX 7900 XTX

AMD

CompatibleRuns Qwen2.5 Coder 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns Qwen2.5 Coder 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Qwen2.5 Coder 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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

Frequently asked questions

Can the Arc A770 16 GB run Qwen2.5 Coder 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. Estimated speed is at most 4.5 tokens/s: usable, but slow.
Which quantization should I use on the Arc A770 16 GB?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the Arc A770 16 GB?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Qwen2.5 Coder 32B Instruct (Arc A770 16 GB)?
We estimate 2.5–4.5 tok/s (estimate, confidence low). Based on: memory bandwidth (560 GB/s) and model size, system RAM bandwidth (~51 GB/s).