Mistral Small 24B Instruct 2501 (FP32) — 79.4 GBon OVH h100-1-gpu

FP32 OVH h100-1-gpu

Overview

Mistral Small 24B Instruct 2501 is a 23.57B parameter dense language model by Mistral AI, with code, multilingual, tool-calls capabilities. It supports a context window of up to 32,768 tokens.

Mistral Small 24B Instruct 2501 is a 23.57-billion-parameter dense transformer from Mistral AI, optimized for instruction following, code generation, and multilingual conversation. It occupies a mid-range parameter class that offers strong performance relative to its size, competing with larger 30B models on many benchmarks. The model supports tool calling and 10 languages including English, French, Chinese, and Japanese. With a 32K context window and flash attention, it fits on a single consumer GPU at Q4 quantization for efficient self-hosted inference.

At FP32 quantization (full-precision quality tier), the model weighs 87.82 GB. This exceeds the 80 GB of VRAM on OVH h100-1-gpu. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.

The NVIDIA H100 80 GB is a datacenter GPU with 80 GB of HBM3 VRAM and 3350 GB/s memory bandwidth. It delivers 1979 FP16 TFLOPS on the Hopper architecture, making it the fastest single-GPU option for large language model inference. It handles models up to 70B parameters with high throughput. Built for datacenter teams running demanding production AI workloads.

Hardware Requirements

Model size	87.82 GB
VRAM available	80 GB
VRAM used	79.4 GB
System RAM	350 GB
Min RAM required	11 GB
GPU layers	35 / 40
Context size	6,882
Backend	cuda13
Flash attention	Yes

Performance Notes

Reduced performance

5 of 40 layers will run on CPU, which may reduce inference speed.

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/mistral-small-24b-instruct-2501/fp32/nvidia-h100-80gb.yaml) apply

Generated values.yaml

/values/mistral-small-24b-instruct-2501/fp32/nvidia-h100-80gb.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o mistral-small-24b-instruct-2501.gguf "https://huggingface.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF/resolve/main/Mistral-Small-24B-Instruct-2501-f32/Mistral-Small-24B-Instruct-2501-f32-00001-of-00003.gguf"

Start Server

llama-server \
  -m mistral-small-24b-instruct-2501.gguf \
  --n-gpu-layers 35 \
  --ctx-size 6882 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Mistral Small 24B Instruct 2501 (FP16)
• Mistral Small 24B Instruct 2501 (Q8_0)
• Mistral Small 24B Instruct 2501 (Q5_K_M)

Other hardware

• Mistral Small 24B Instruct 2501 on CPU Only
• Mistral Small 24B Instruct 2501 on NVIDIA A100 80GB
• Mistral Small 24B Instruct 2501 on NVIDIA RTX 3090

Frequently Asked Questions

How much VRAM does Mistral Small 24B Instruct 2501 (FP32) need?

The FP32 quantization of Mistral Small 24B Instruct 2501 requires 87.82 GB. 35 of 40 layers fit in the 80 GB of VRAM on OVH h100-1-gpu; remaining layers are offloaded to CPU.

Can I run Mistral Small 24B Instruct 2501 on OVH h100-1-gpu?

Yes, with reduced performance. OVH h100-1-gpu can run Mistral Small 24B Instruct 2501 (FP32), but only 35 of 40 layers fit in VRAM. The rest are offloaded to CPU.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. FP32 compresses Mistral Small 24B Instruct 2501 from its original size down to 87.82 GB.

What quantization should I choose for Mistral Small 24B Instruct 2501?

FP32 is a full-precision quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

What is flash attention and why is it enabled?

Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Mistral Small 24B Instruct 2501 on OVH h100-1-gpu, flash attention is enabled to maximise context length and throughput within the available 80 GB of VRAM.

Why are some layers offloaded to CPU?

OVH h100-1-gpu has 80 GB of VRAM, but Mistral Small 24B Instruct 2501 (FP32) requires approximately 87.82 GB. Only 35 of 40 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

How do I run Mistral Small 24B Instruct 2501 (FP32) with Ollama?

Run ollama run mistral-small:24b-instruct-2501-fp32 to start Mistral Small 24B Instruct 2501 (FP32). Ollama handles downloading the model weights automatically on first run.

Last updated: March 20, 2026