MistralAI releases Mistral Small 3.1: Multimodal and 128k context fitting a 4090 RTX or 32GB Mac | 5 basic components of cpu | Cpu hardware examples | Intel hardware | Turtles AI

Mistral Small 3.1 is a newly introduced AI model from Mistral AI, designed to deliver high performance in a compact form factor. With 24 billion parameters, this model has been optimized for latency, ensuring fast and accurate responses. Released under the Apache 2.0 license, Mistral Small 3.1 is available for both commercial and non-commercial purposes, providing flexibility to developers and enterprises.

Key Points:

• Efficiency: Designed to run on standard hardware, such as a single RTX 4090 or a MacBook with 32GB of RAM, once quantized.
• Multimodality: Capable of understanding and analyzing both text and images, expanding its applications in various industries.
• Large context window: Supports a context length of up to 128,000 tokens, allowing processing of large documents without compromising performance.
• Competitive Performance: Internal benchmarks show it outperforms competing models such as the Gemma 3 and GPT-4o Mini, offering inference at 150 tokens per second.

Mistral Small 3.1 stands out for its ability to run on resource-constrained devices, making it ideal for local applications and organizations that handle sensitive data. Its multimodal nature allows it to analyze both text and images, opening up application possibilities in areas such as virtual assistant, document processing, and visual analytics. The extended context window of 128,000 tokens allows the model to handle long and complex documents, while maintaining consistency and accuracy in its responses. According to internal benchmarks, Mistral Small 3.1 outperforms competing models such as the Gemma 3 and GPT-4o Mini, offering fast inference at 150 tokens per second. However, there are currently no independent external benchmarks available to confirm this performance. While support for devices such as MacBooks with 32GB of RAM has been mentioned, there are currently no quantized versions released in formats such as GGUF or MLX. It is likely that the community will release these versions soon to facilitate adoption on a wider range of hardware. The Apache 2.0 license allows for a wide range of uses, both commercial and non-commercial, giving companies the ability to customize the model for specific needs.

Mistral Small 3.1 represents a significant step forward in offering powerful and versatile AI models, while maintaining a balance between high performance and affordable hardware requirements.