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Home/Models & Labs
Models & Labs

Ollama Enhances Model Scheduling System

Ollama Blog·September 23, 2025·medium confidence

Why it matters

  • →This improvement can lead to more efficient use of resources in AI model training and deployment, which is crucial for practitioners working with large models.
Ollama Enhances Model Scheduling System
©Ollama Blog

Ollama has introduced an improved model scheduling system that aims to reduce crashes from out of memory issues and enhance GPU utilization and performance, particularly on multi-GPU setups.

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Ollama's latest update to its MLX engine significantly enhances performance on Apple Silicon, leveraging Apple's unified memory and Metal-backed framework. The introduction of NVIDIA's NVFP4 format allows for higher quality outputs while maintaining efficiency, making it easier to transition models between datacenter and desktop environments. The update also includes optimizations that increase processing speed by 20% and improve responsiveness in agent workflows through a new snapshot system. This release marks a notable step forward in making high-performance AI more accessible on consumer hardware.

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Llama.cpp b9626 Release Adds Cohere2-MoE Support

The latest b9626 release of llama.cpp introduces architectural support for the cohere2-MoE model, marking a significant update for developers working with this model. This release also includes various technical improvements such as the removal of redundant checks and enhancements in tensor handling, which streamline the model's performance. By adding cohere2moe to the Llama Model Saver supported list, the update broadens the toolkit available for AI practitioners. While these changes may seem incremental, they collectively enhance the robustness and flexibility of llama.cpp, making it a more versatile tool for AI development.

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llama.cpp b9627 Release Expands Platform Support

The b9627 release of llama.cpp continues to enhance its platform reach, though it doesn't introduce any groundbreaking features. This update includes support for a wide array of systems, from macOS and iOS to various Linux distributions and Windows configurations, including CUDA and Vulkan support. Notably, the release maintains its focus on making llama.cpp a versatile tool across different hardware setups, but it doesn't introduce new model architectures or quantization methods. This iteration is more about solidifying its presence across multiple operating systems rather than introducing novel capabilities.

llama.cpp Releases·Jun 14, 2026