Mistral Nemo

What is Mistral Nemo?

Mistral Nemo is Mistral AI + NVIDIA's open-weights language model. Released in 2024-07, it ships in 1 size (12B) and is licensed Apache 2.0. The most popular use case is 12B class chat with 128K context.

VRAM and hardware

The 12B size fits in 8 GB of VRAM.

Size	Min VRAM (Q4_K_M)	Recommended VRAM	Tokens/sec on 3090
12B	8 GB	12 GB	~22 tok/s

How to run Mistral Nemo locally

Option 1: Ollama (simplest)

ollama pull mistral-nemo
ollama run mistral-nemo

Option 2: Mullama (production)

mullama pull mistral-nemo
mullama run mistral-nemo

Option 3: llama.cpp (CLI)

# Download a GGUF from Hugging Face (search "mistral-nemo gguf")
./llama-cli -m mistral-nemo.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-nemo.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 Nemo

• chat
• RAG
• long-context
• tools

Hardware it fits on

• Apple Silicon (8GB): 12B in MLX
• Apple Silicon (16GB): 12B in MLX
• Apple Silicon (32GB): 12B in MLX or GGUF
• Apple Silicon (64GB+): 12B in MLX
• RTX 3090 (24GB): 12B at Q4_K_M
• RTX 4090 (24GB): same as 3090, ~30% faster
• RTX 5090 (32GB): 12B at Q8_0
• 2× RTX 6000 Ada (96GB total): 12B at Q4_K_M

Related models in the Mistral family

• Mistral — 7B · legacy 7B chat, very low resource
• Mistral Small — 22B, 24B · sub-30B quality on consumer hardware
• 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