
Together AI has launched automated canary rollouts for its inference endpoints, enabling staged model swaps with built-in safety checks. The system allows users to define traffic ladders (e.g., 5% to 100%) and metric gates, such as p95 latency or error rates, which automatically pause the rollout if performance degrades. This feature addresses the operational risks of deploying new foundation models by replacing manual monitoring with platform-enforced health checks and automatic rollback capabilities. The tool is available via CLI, REST API, and Python SDK for existing Together AI users.
Read originalThis release stabilizes Claude Code's core reliability by fixing persistent bugs in session resumption and prompt caching that previously caused data loss or infinite loops. It also tightens enterprise security with new Bedrock upstream support for IAM role assumption and mandatory guardrail application. The update addresses critical edge cases like proxy stream drops and oversized tool calls, ensuring smoother operation in complex development environments.
This release targets a specific bottleneck in long-context inference by optimizing the sparse flash attention prefill step for NVIDIA GPUs. By templating kernels to unroll loops at compile time, batched sparse operations drop from 586 microseconds to 244 microseconds on 49k context windows. This isn't just a generic speed bump; it makes handling very long documents significantly more efficient for users relying on sparse attention mechanisms. The change is already baked into the standard CUDA builds, requiring no special flags.
The latest llama.cpp build brings immediate relevance to users on bleeding-edge NVIDIA hardware with native CUDA 13.4 support across Linux and Windows, closing the gap for those testing next-gen GPU architectures. More notably, it finally addresses the mobile inference landscape by including a dedicated build for Linux arm64 Snapdragon devices, covering CPU, Adreno GPU, and Hexagon NPU paths. This moves local AI beyond just desktop GPUs into the realm of high-performance edge computing on Qualcomm silicon. While Apple Silicon builds have KleidiAI disabled in this specific release, the expansion to ARM-based mobile NPUs marks a significant shift in where llama.cpp can run efficiently.