Mistral Small
Mistral's sub-30B model. Best in class for its size; competitive with 70B+ on chat.
Best for sub-30B quality on consumer hardware
Sizes 22B · 24B
Context 32K
License MRL License (open-weights)
Min VRAM (default size, Q4) 15 GB
Rec VRAM 24 GB
What is Mistral Small?
Mistral Small is Mistral AI's open-weights language model. Released in 2025-03, it ships in 2 sizes (22B, 24B) and is licensed MRL License (open-weights). The most popular use case is sub-30B quality on consumer hardware.
VRAM and hardware
The smallest 22B size needs at least 15 GB VRAM; the largest 24B needs around 15 GB.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| 22B | 15 GB | 23 GB | ~19 tok/s |
| 24B | 15 GB | 23 GB | ~19 tok/s |
How to run Mistral Small locally
Option 1: Ollama (simplest)
ollama pull mistral-small
ollama run mistral-small Option 2: Mullama (production)
mullama pull mistral-small
mullama run mistral-small Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "mistral-small gguf")
./llama-cli -m mistral-small.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("mistral-small.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Mistral Small
- chat
- RAG
- tools
- agentic
Hardware it fits on
- Apple Silicon (8GB): 22B in MLX
- Apple Silicon (16GB): 24B in MLX
- Apple Silicon (32GB): 24B in MLX or GGUF
- Apple Silicon (64GB+): 24B in MLX
- RTX 3090 (24GB): 24B at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): 24B at Q8_0
- 2× RTX 6000 Ada (96GB total): 24B at Q4_K_M
Related models in the Mistral family
- Mistral — 7B · legacy 7B chat, very low resource
- Mistral Nemo — 12B · 12B class chat with 128K context
- Mistral Large — 123B · frontier-tier chat on a single 96GB GPU
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware