
Together AI has unveiled a new architecture for model inference that combines endpoints, deployments, and configurations with a capacity-aware traffic split. This setup allows for advanced features like rollouts and A/B testing while maintaining zero-downtime updates. The platform uses immutable configurations to ensure consistent performance and easy rollback options. This approach simplifies the deployment process and enhances the reliability of AI applications by optimizing resource allocation and scaling.
Read originalThe latest b10156 release of llama.cpp continues its trend of broadening platform compatibility, notably adding support for ROCm 7.2 on Ubuntu x64. This update ensures that AMD GPU users can leverage llama.cpp more effectively, narrowing the gap with NVIDIA's CUDA. The release also includes Vulkan support for both Ubuntu and Windows, enhancing the versatility of the software for developers. While no new models or quantization methods are introduced, this update solidifies llama.cpp's position as a versatile inference runtime across diverse hardware configurations.
The latest b10164 release of llama.cpp focuses on improving CUDA performance, particularly for Mamba-2 prefill acceleration. By introducing chunked SSD matrix multiplication, the update aims to enhance efficiency and memory coalescing. This release also addresses several technical fixes, including resolving a read-write race condition in CUDA operations. While there are no groundbreaking new features, these optimizations make llama.cpp a more robust choice for developers working with CUDA and related technologies.
The latest b10166 release of llama.cpp focuses on refining output handling, particularly by addressing issues with views in outputs. This update introduces changes like setting outputs to view sources and ensuring consistent logits handling, which are crucial for developers working with complex AI models. The release also simplifies the set_outputs function, making it more efficient for users. While there are no groundbreaking new features, these improvements contribute to a more stable and reliable platform for AI development.