Mistral Small 3.2 24B

When to pick this model

• Self-hosted multilingual chat assistant on a single 24GB GPU
• Agentic workflows that need reliable tool calls without paying for a frontier model
• OCR and document Q&A pipelines combining text and screenshots
• European deployments needing strong French, German, and Spanish coverage
• Drop-in upgrade for existing Small 3.1 deployments

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Mistral Small 3.2 24B needs a 16 GB card at Q4_K_M (14 GB). Stepping up to Q8_0 nearly doubles the footprint to 26 GB, and unquantized FP16 weights take 48 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Mistral Small 3.2 24B needs roughly 24 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 4 tokens/sec on entry-level GPUs, on the order of 15 tokens/sec on a mid-range card, and up to 40 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Mistral Small 3.2 24B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run Mistral Small 3.2 24B?

To run Mistral Small 3.2 24B locally at Q4, you need ~14 GB for Q4 weights alone. Hardware option to compare: RTX 5070 Ti 16GB (GIGABYTE Gaming OC). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Strengths

• Roughly 50% fewer infinite-generation loops than 3.1
• Notably improved function calling and structured output reliability
• Vision encoder included for multimodal tasks
• Apache 2.0 — unrestricted commercial use
• Fits comfortably on a single 24GB consumer GPU at Q4

Limitations

• Requires a recent Ollama build for full chat-template support
• Still trails frontier models on hard reasoning benchmarks
• Vision quality lags dedicated VLMs like Qwen2.5-VL

Typical workloads

In our catalog grid, Mistral Small 3.2 24B is filed under Advanced Chat, Vision, Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; multilingual workloads; French-language output where quality matters.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense 24B · vision · Tekken tokenizer

Training: Minor update to Small 3.1 (2503).

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

ollama run mistral-small3.2:24b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Mistral Small 3.2 24B need?

At the recommended Q4_K_M quantization, Mistral Small 3.2 24B needs about 14 GB of VRAM. Q8_0 takes 26 GB, and unquantized FP16 weights take 48 GB.

Can Mistral Small 3.2 24B run without a GPU?

Yes — with roughly 24 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Mistral Small 3.2 24B support?

Mistral Small 3.2 24B supports a 125k-token context window (128,000 tokens).

Can I use Mistral Small 3.2 24B commercially?

Yes. Mistral Small 3.2 24B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Mistral Small 3.2 24B on consumer hardware?

Our compatibility engine estimates on the order of 15 tokens/sec on a mid-range GPU and up to 40 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Mistral Small 3.2 24B should I download first?

Start with Q4_K_M (14 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q5_K_M.