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Run LLM Locally

Mistral Small 3.2 24B Instruct on the Arc A770 16 GB

Mistral Small 3.2 24B Instruct at Q4_K_M needs about 16.2 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 fits entirely.

Mistral Small 3.2 24B Instruct

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 16.2 GB but only 15.5 GB of VRAM is usable: 0.7 GB must be loaded into system RAM. It runs, 96% on the GPU, but more slowly.

Your hardware
Arc A770 16 GB · 64 GB RAM
Estimated memory
~16.2 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (0.7 GB)
Generation speed
8.5–17 tok/s estimate
Largest context in fast memory
3K tokens
Confidence: low

Based on: memory bandwidth (560 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~16.2 GB (15.6 GB–16.9 GB) needed · estimate

0.7 GB must be offloaded to system RAM

  • Model weights13.3 GB
  • KV cache1.3 GB
  • Runtime overhead0.8 GB
  • Safety margin0.8 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

Mistral Small 3.2 24B Instruct on Arc A770 16 GB + 64 GB RAM · 8K context
Mistral Small 3.2 24B Instruct Q8_08K~26.7 GB · 1.6–3 tok/s estimatePossible but slow
Mistral Small 3.2 24B Instruct Q6_K8K~21.1 GB · 3–5.5 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct Q5_K_M8K~18.6 GB · 4.5–8.5 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct Q4_K_M8K~16.2 GB · 8.5–17 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct IQ4_XS8K~14.7 GB · 16–25 tok/s estimateCompatible
Mistral Small 3.2 24B Instruct Q3_K_M8K~13.4 GB · 18–27 tok/s estimateCompatible

Alternatives

Hardware that runs Mistral Small 3.2 24B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XT

AMD

ExcellentRuns Mistral Small 3.2 24B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns Mistral Small 3.2 24B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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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 Mistral Small 3.2 24B Instruct?
The model needs about 16.2 GB but only 15.5 GB of VRAM is usable: 0.7 GB must be loaded into system RAM. It runs, 96% on the GPU, but more slowly.
Which quantization should I use on the Arc A770 16 GB?
IQ4_XS is the highest-precision quantization that fits entirely in VRAM at 8K context. ~14.7 GB / 15.5 GB.
What is the largest context on the Arc A770 16 GB?
At Q4_K_M, about 3K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Mistral Small 3.2 24B Instruct (Arc A770 16 GB)?
We estimate 8.5–17 tok/s (estimate, confidence low). Based on: memory bandwidth (560 GB/s) and model size, system RAM bandwidth (~51 GB/s).