LLM
How to deploy Mistral-Small-24B-Instruct-2501 on a GPU cloud
A 23.6B language model for chat and instruction-following. Full specs, license and use cases.
Mistral-Small-24B-Instruct-2501 size and hardware requirements
23.6B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
52.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 43.9 GB | 52.7 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 22.0 GB | 26.3 GB | RTX 4080 Super | 1 | $0.383/hr |
| INT4 (quantized) | 11.0 GB | 13.2 GB | RTX A4000 | 1 | $0.113/hr |
How to run Mistral-Small-24B-Instruct-2501
Run Mistral-Small-24B-Instruct-2501 with vLLM
From mistralai/Mistral-Small-24B-Instruct-2501's own deployment docs.
vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choiceSource: https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501/raw/main/README.md
Run Mistral-Small-24B-Instruct-2501 with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Mistral-Small-24B-Instruct-2501-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Mistral-Small-24B-Instruct-2501-GGUFSource: https://huggingface.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF
Deploy Mistral-Small-24B-Instruct-2501 on Aquanode
Aquanode has no one-click deploy template for Mistral-Small-24B-Instruct-2501; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× A100 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod