llama.cpp b9444 adds weak ETag support for faster server caching
The popular LLM inference library improves HTTP server efficiency with ETag handling.
llama.cpp, the widely-adopted open-source library for running large language models on consumer hardware, has shipped version b9444. With over 114,000 GitHub stars and 19,000 forks, it's a staple for developers deploying LLMs locally. The new release focuses on the HTTP server component, adding support for If-None-Match weak ETags (issue #23916). This feature allows the server to respond with a 304 Not Modified status when cached content hasn't changed, drastically reducing bandwidth and latency for clients issuing repeated requests.
This optimization is particularly valuable for production setups where llama.cpp serves as an inference backend for web applications or APIs. By leveraging weak ETags, the server can validate cache freshness without heavy computation. The release also includes pre-built binaries for a wide range of platforms: Apple Silicon, Intel macOS, Linux (CPU, Vulkan, ROCm, OpenVINO, SYCL), Windows (CPU, CUDA, Vulkan, HIP), and Android arm64. Developers can upgrade via GitHub or build from source. While a minor version bump, this update underscores the project's commitment to polish and performance at scale.
- Handles If-None-Match weak ETags to enable HTTP 304 responses and reduce data transfer
- Optimized server caching for repeated API calls, lowering latency for frequent queries
- Available across 15+ build targets including macOS, Linux, Windows, and Android
Why It Matters
Faster caching in llama.cpp reduces inference latency for AI applications, improving user experience at scale.