Models & Labs
Together AI Introduces Autoscaling for LLM Inference
Together AI Bloghigh confidence
Why it matters
- →Autoscaling based on inference-native metrics optimizes resource use for LLM deployments.
- →It addresses the limitations of traditional autoscaling methods in handling LLM traffic.
- →This feature is crucial for managing costs and performance in GPU-constrained environments.
Together AI has launched a new autoscaling feature for large language model (LLM) inference on its platform. This feature allows deployments to scale based on specific metrics such as in-flight requests and GPU utilization, addressing the unique challenges of LLM serving. Traditional autoscaling methods often fail due to misleading metrics and lengthy cold starts, but Together AI's approach offers a catalog of inference-native metrics for more precise scaling. This advancement helps optimize resource allocation, especially in environments where GPU resources are limited.
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Llama.cpp b10208 Release Enhances SYCL Performance
The latest b10208 release of llama.cpp introduces significant improvements in SYCL performance, particularly with the addition of oneMKL GEMM flash attention for XMX-accelerated prompt processing. This update addresses previous issues with interleaved destination layouts in the normalize kernel, ensuring more accurate attention outputs across models. By removing redundant stream waits and refining MKL FA dispatch gates, the release optimizes processing speeds, nearly doubling performance in some cases. These enhancements make llama.cpp a more robust and efficient tool for developers working with large language models.
llama.cpp Releases
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llama.cpp b10211 Release Expands Platform Support
The latest b10211 release of llama.cpp continues its trend of broadening platform compatibility, making it a versatile tool for developers across various systems. Notably, this update includes support for Ubuntu with ROCm 7.2, enhancing performance for AMD GPU users. Windows users benefit from the inclusion of CUDA 12 and 13 DLLs, ensuring compatibility with the latest NVIDIA technologies. While the release doesn't introduce new model architectures, it solidifies llama.cpp's position as a flexible inference runtime across diverse hardware configurations.