Mistral Mistral AI Released 2025-03 chattoolsinstruction

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