Mistral Small 3.2 24B

Positioning

Mistral Small 3.2 24B is a dense 24-billion-parameter model from Mistral AI, released under the permissive Apache 2.0 license. It is an iterative refresh of the earlier Mistral Small 3 24B, retaining the same architecture while improving instruction-following and tool-call reliability. With a 131,072-token context window, it targets consumer-tier multilingual instruction-following tasks, making it a strong candidate for local deployment on single-GPU setups.

Strengths

• Apache 2.0 license: Permissive licensing allows unrestricted commercial use, modification, and redistribution, making it ideal for proprietary applications.
• Large 128K context window: The 131,072-token context enables processing of long documents, extended conversations, or complex multi-turn tasks without truncation.
• Consumer-friendly size: At 24B dense parameters, the model fits within consumer GPU memory at common quantizations (e.g., Q4_K_M ~13.5 GB plus overhead), enabling local inference on a single 24GB GPU.
• Improved instruction following: This refresh specifically targets better adherence to instructions and more reliable tool-call behavior, enhancing practical utility for agentic workflows.

Limitations

• No community benchmarks available: We do not yet have independent, community-reported benchmark scores for this model. Published vendor metrics should be treated as best-case until verified by third parties.
• Dense architecture at 24B: Unlike Mixture-of-Experts models that activate only a fraction of parameters, this dense model uses all 24B parameters per forward pass, resulting in higher compute and memory requirements relative to MoE models of similar total parameter count.
• Quantization trade-offs: Running at lower quantizations (e.g., Q4_K_M) reduces memory footprint but may degrade output quality compared to FP16. Operators should test their specific use case to find the acceptable balance.
• No multimodal support: This model is text-only; it does not natively process images or other modalities, limiting its applicability in vision-language tasks.

What it takes to run this locally

At FP16 precision, the model requires 48 GB of disk space and roughly 48 GB of GPU memory, exceeding consumer GPU capacity. However, quantized versions drastically reduce the memory requirement: Q8_0 (26 GB), Q6_K (19.8 GB), Q5_K_M (17.1 GB), Q4_K_M (13.5 GB), Q3_K_M (11.7 GB), and Q2_K (~7.8 GB). For typical use with a 131K context, add ~30-50% for KV cache and framework overhead. This means a single 24GB consumer GPU (e.g., RTX 4090) can run the Q4_K_M quant comfortably, while Q3_K_M or Q2_K may fit on 12-16GB cards. For FP16 or Q8_0, a workstation GPU with 48GB or dual consumer GPUs would be necessary.

Should you run this locally?

Yes if you need a permissively licensed, dense 24B model for instruction-following or tool-calling tasks and have a single 24GB GPU (or can use Q3_K_M on 12-16GB). The Apache 2.0 license makes it particularly attractive for commercial deployment without legal overhead.

No if you require multimodal capabilities, need the absolute lowest memory footprint (consider MoE models with smaller active parameters), or are risk-averse to using a model without independent benchmark validation.

Catalog cross-links

• Mistral Small 3 24B
• Mistral 7B
• Mistral AI vendor page