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Run LLM Locally

Mistral Small 3.2 24B Instruct on the RTX 5070 Ti

Mistral Small 3.2 24B Instruct at Q4_K_M needs about 16.1 GB, more than the 16 GB of the RTX 5070 Ti: 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 16.1 GB but only 15.5 GB of VRAM is usable: 0.6 GB must be loaded into system RAM. It runs, 96% on the GPU, but more slowly.

Your hardware
RTX 5070 Ti 16 GB · 64 GB RAM
Estimated memory
~16.1 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (0.6 GB)
Generation speed
17–31 tok/s estimate
Largest context in fast memory
4K tokens
Confidence: low

Based on: memory bandwidth (896 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~16.1 GB (15.5 GB–16.8 GB) needed · estimate

0.6 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 (CUDA) · 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 RTX 5070 Ti

Mistral Small 3.2 24B Instruct on RTX 5070 Ti + 64 GB RAM · 8K context
Mistral Small 3.2 24B Instruct Q8_08K~26.6 GB · 1.7–3.5 tok/s estimatePossible but slow
Mistral Small 3.2 24B Instruct Q6_K8K~21 GB · 3.5–6.5 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct Q5_K_M8K~18.5 GB · 6–11 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct Q4_K_M8K~16.1 GB · 17–31 tok/s estimateCPU/GPU offload
Mistral Small 3.2 24B Instruct IQ4_XS8K~14.6 GB · 46–63 tok/s estimateCompatible
Mistral Small 3.2 24B Instruct Q3_K_M8K~13.3 GB · 50–69 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

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Frequently asked questions

Can the RTX 5070 Ti run Mistral Small 3.2 24B Instruct?
The model needs about 16.1 GB but only 15.5 GB of VRAM is usable: 0.6 GB must be loaded into system RAM. It runs, 96% on the GPU, but more slowly.
Which quantization should I use on the RTX 5070 Ti?
IQ4_XS is the highest-precision quantization that fits entirely in VRAM at 8K context. ~14.6 GB / 15.5 GB.
What is the largest context on the RTX 5070 Ti?
At Q4_K_M, about 4K 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 (RTX 5070 Ti)?
We estimate 17–31 tok/s (estimate, confidence low). Based on: memory bandwidth (896 GB/s) and model size, system RAM bandwidth (~51 GB/s).