Mistral Small 24B: Hardware & Business Fit
- Tools
- Code
- Multilingual
- Long context
An Apache-2.0 mid-size model that competes with larger ones on many tasks. Verify the exact release; figures are approximate.
- Parameters
- ~24B
- Context
- ~32K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~14GB
What Mistral Small 24B is good for
- ▸Mid-tier business agent
- ▸RAG
- ▸Back-office automation
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~14GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~25GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~48GB | Full precision; largest footprint, best quality. |
Run Mistral Small 24B locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run mistral-small:24bmistralai/Mistral-Small-24B-Instruct-2501Compatible hardware
Devices from our catalog graded for Mistral Small 24B, best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~48GB) with ~121GB headroom — about 3 concurrent instances.
FP16 · ~48GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~48GB) with ~515.2GB headroom — about 11 concurrent instances.
FP16 · ~48GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~48GB) with ~515.2GB headroom — about 11 concurrent instances.
FP16 · ~48GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~48GB) with ~121GB headroom — about 3 concurrent instances.
FP16 · ~48GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~48GB) with ~110.4GB headroom — about 3 concurrent instances.
FP16 · ~48GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~48GB) with ~76.1GB headroom — about 2 concurrent instances.
FP16 · ~48GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~48GB) with ~76.1GB headroom — about 2 concurrent instances.
FP16 · ~48GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~48GB) with ~22.4GB headroom — about 1 concurrent instance.
FP16 · ~48GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~48GB) with ~22.4GB headroom — about 1 concurrent instance.
FP16 · ~48GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~48GB) with ~36.5GB headroom — about 1 concurrent instance.
FP16 · ~48GBRuns well
Use inside the AI Business OS
Mistral Small 24B suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run Mistral Small 24B?+
At 4-bit you need roughly ~14GB of usable memory. The minimum self-hostable option in our catalog is the Intel Arc A770 16GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for Mistral Small 24B?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run Mistral Small 24B locally or in the cloud?+
Local-first is recommended for Mistral Small 24B. It fits comfortably on hardware you can own, keeping data private and costs predictable.
Other sizes in the Mistral family
All Mistral models →Same family, different size. Pick the variant that fits your hardware.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use Mistral Small 24B inside your AI Business OS
BrainOutput helps you run Mistral Small 24B as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS