Mistral AI launches Magistral Small 1.1 – powerful AI inference on RTX 4090
Mistral AI has released Magistral Small 1.1, a 24-billion parameter model capable of running locally on a single Nvidia RTX 4090 graphics card, featuring open reasoning steps.
What happened?
Mistral AI has launched Magistral Small 1.1, a compact 24-billion parameter AI model designed for local inference. The model is optimised to run locally on a single consumer-grade Nvidia RTX 4090 graphics card. A key technical innovation is the introduction of an open reasoning mechanism via specific [THINK] tags, which display the model's chain of thought, making the decision-making process transparent and auditable.
Key facts
| Modellstorlek | 24 miljarder parametrar |
|---|---|
| Filstorlek (GPTQ) | 12 GB |
| Hårdvarukrav | 1x Nvidia RTX 4090 |
| Lanseringsdatum | 1 oktober 2026 |
Why it matters
The model addresses the conflict between performance, cost, and data integrity that has affected many companies throughout 2025 and 2026. By running advanced reasoning models locally on standard hardware, companies can drastically reduce API costs while keeping sensitive data within their own networks. The open display feature also increases traceability in sectors that require audits and compliance oversight.
Who is affected?
The launch is primarily aimed at small and medium-sized enterprises, developers, and organisations in sensitive sectors such as finance and healthcare. It is designed for users who require high reasoning capability in AI applications but wish to avoid high API costs or are prohibited from sending sensitive data to cloud services.
Impact on the EU
The model is distributed under an open licence, making it accessible to European companies and developers. Its ability to run locally facilitates compliance with strict EU regulations such as the GDPR and the requirements of the EU AI Act regarding data integrity and transparency.
What else you should know
Using GPTQ quantisation, the model's size is compressed to 12 GB, retaining 92 per cent of the precision compared to the FP16 format while fitting on consumer hardware. In Mistral's internal tests, the transparency feature using [THINK] tags increased user confidence in the model's conclusions by 62 per cent.
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