# Ollama v0.40.15 adds MiMo V2.6 support and concurrent GGUF request handling

> **Key takeaway:** Ollama expands its model library with Xiaomi's MiMo and AutoTrust decision models while improving concurrency for recurrent and vision GGUF models.

**Published:** 2026-11-06T01:47:18+00:00 
**Source:** Ollama Local AI Runtimes 
**Category:** ai-local-edge 
**Canonical URL:** https://fosswire.org/news/ollama-v04015-adds-mimo-v26-support-and-concurrent-gguf-requ.html 

## Summary
Ollama has released version 0.40.15, introducing support for several new specialized model families. This includes Xiaomi's MiMo V2.6, a 9B parameter model optimized for coding and agentic workflows, and the AutoTrust decision model family, which provides probabilistic ratings and yes/no responses for text and image inputs.

Beyond model additions, the update improves the runtime's efficiency and integration capabilities. Key technical improvements include the ability for recurrent and vision-based GGUF models to handle multiple simultaneous requests and support for long-context YaRN scaling for Qwen3 and Qwen3.5 models. The release also enhances the user experience for CLI and desktop integrations like Kimi Code CLI and Claude Desktop.

## Technical improvements in Ollama v0.40.15
- **Model Support:** Added Xiaomi MiMo V2.6 (9B) for code/agent tasks and AutoTrust decision models (9b, 35b, and 27b-vl), with the latter supporting Apple silicon.
- **Concurrency Enhancements:** Recurrent and vision-based GGUF models are now capable of serving more than one request at a time.
- **Context Scaling:** Implementation of long-context support using YaRN scaling for Qwen3 and Qwen3.5 model architectures.
- **Tooling Integration:** Improved interoperability with Kimi Code CLI (including migration paths) and automated model list population for Pi and Claude Desktop.

## Why it matters
By enabling concurrent requests for vision and recurrent GGUF models, Ollama increases its utility for developers building multi-user or high-throughput local AI applications. The addition of specialized decision models and long-context scaling further positions the runtime as a robust platform for complex, agentic local AI workflows.