Models & Labs
DeepSeek-V4 Tackles Million-Token Context Challenge
Together AI Bloghigh confidence
Why it matters
- →DeepSeek-V4 shifts the focus from model architecture to serving systems for handling large context windows.
- →The model's hybrid attention design reduces cache pressure, improving efficiency in long-context workloads.
- →Effective memory management and request batching make long-context inference more practical and economically viable.
Together AI's DeepSeek-V4 introduces a novel approach to managing large context windows in AI models by focusing on serving systems. The model uses a hybrid attention design to compress context before key-value storage, reducing cache pressure and improving efficiency. This architectural change allows for better handling of long-context workloads, particularly on NVIDIA's HGX B200 hardware. The model's success hinges on how well the inference engine can manage different cache types, making long-context inference more feasible and cost-effective.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
How we got here
More in Models & Labs
Models & Labsmodels
llama.cpp b12029 adds CUDA 13 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
llama.cpp Releases
© The Verge AIModels & Labsother
