
Together AI has launched a significant update to its inference platform, designed to give developers greater control over AI model deployment. The platform supports rapid model deployment with features like canary and blue-green updates, ensuring production-grade reliability. A closed beta for custom training, including reinforcement learning and fine-tuning, is also announced. This update emphasizes the use of open-weight models, allowing teams to integrate proprietary IP securely while maintaining control over performance and quality. The platform aims to streamline AI deployment, making it more flexible and efficient.
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This release quietly closes the hardware gap for local inference by adding default support for CUDA 13 and ROCm 10.0 across Linux and Windows. Users with newer NVIDIA GPUs or AMD accelerators no longer need to compile from source to get performance, while Apple Silicon builds now ship KleidiAI disabled by default, shifting the focus back to standard ARM optimizations. The inclusion of Snapdragon support for Linux and Android marks a significant step toward on-device AI beyond the x86/ARM desktop ecosystem. It is less about new features and more about ensuring llama.cpp remains the universal runtime for every available accelerator.
Speculative decoding just got a significant speed boost on NVIDIA GPUs. This release introduces a specialized kernel for 'few-row' matrix multiplications, targeting the small batch sizes typical of verification passes in speculative decoding workflows. By optimizing these specific operations for quantized formats like Q8_0 and IQ4 variants, llama.cpp reduces latency where it matters most during token generation. The change is CUDA-only for now but leverages hardware capabilities from Ampere onwards, making local inference faster without requiring new model architectures or complex configuration changes.
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Together AI Enhances Open-Weight Inference Platform
3 developments