Mistral Small 24B Instruct 2501 (FP32) — 23.4 GBon OVH a10-1-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 24 GB of VRAM on OVH a10-1-gpu. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.
Hardware Requirements
| Model size | 87.82 GB |
| VRAM available | 24 GB |
| VRAM used | 23.4 GB |
| System RAM | 40 GB |
| Min RAM required | 65.9 GB |
| GPU layers | 10 / 40 |
| Context size | 1,050 |
| Backend | cuda13 |
| Flash attention | Yes |
| Reading from disk | Yes |
Performance Notes
Deploy
Prerequisites
Ensure your GPU nodes are prepared with the NVIDIA container toolkit:
ansible-playbook prositronic.infra.nvidia_container_toolkitCommand
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/mistral-small-24b-instruct-2501/fp32/nvidia-a10.yaml) applyGenerated values.yaml
/values/mistral-small-24b-instruct-2501/fp32/nvidia-a10.yaml
Loading values…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. 10 of 40 layers fit in the 24 GB of VRAM on OVH a10-1-gpu; remaining layers are offloaded to CPU.
Can I run Mistral Small 24B Instruct 2501 on OVH a10-1-gpu?
Yes, with reduced performance. OVH a10-1-gpu can run Mistral Small 24B Instruct 2501 (FP32), but only 10 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 a10-1-gpu, flash attention is enabled to maximise context length and throughput within the available 24 GB of VRAM.
Why are some layers offloaded to CPU?
OVH a10-1-gpu has 24 GB of VRAM, but Mistral Small 24B Instruct 2501 (FP32) requires approximately 87.82 GB. Only 10 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.