Models & Labs
llama.cpp b9388 release enhances Turing support
llama.cpp Releaseshigh confidence
Why it matters
- →Enhances compatibility for Turing architecture, crucial for developers using these GPUs.
- →Addresses JIT compilation issues, improving performance and reliability.
- →Expands platform support, making llama.cpp more versatile for diverse hardware.
The b9388 release of llama.cpp focuses on optimizing support for Turing architecture, specifically addressing JIT compilation issues for SM75 Turing devices. This update adds MMVQ_PARAMETERS_TURING to prevent mismatches when compiling Turing device code on newer architectures like Ampere. The release also includes platform support updates for macOS, Linux, and Windows, though no new models or quantization methods are introduced. This iteration enhances compatibility and performance, reinforcing llama.cpp's utility for developers working across various hardware setups.
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- 1llama.cpp b9200 Release Enhances Performance
llama.cpp Releases · May 18, 2026 · Same story
- 2llama.cpp b9209 Release Expands Platform Support
llama.cpp Releases · May 19, 2026 · Same story
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Open the full story →llama.cpp b9245 Release Expands Platform Support
3 developments
- May 20 · llama.cpp Releases
llama.cpp b9245 Release Expands Platform Support - May 27 · llama.cpp Releases
llama.cpp adds support for Gemma4ForCausalLM↳ llama.cpp adds support for Gemma4ForCausalLM and resolves indentation issues - May 29 · llama.cpp Releases
llama.cpp b9388 release enhances Turing support (This article)↳ b9388 adds MMVQ_PARAMETERS_TURING to optimize JIT compilation for SM75 Turing devices and prevent mismatches on Ampere or newer architecture
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llama.cpp b12022 adds ROCm 10 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally bringing ROCm 10.0 to Linux and Windows alongside CUDA 13.4, effectively closing the hardware gap for AMD users who were previously left behind. The inclusion of native Snapdragon support on Linux marks a significant step toward running local LLMs on ARM-based mobile chips without emulation overhead. While KleidiAI on Apple Silicon is disabled in this build, the broader expansion to diverse accelerators ensures that developers aren't locked into NVIDIA ecosystems anymore.
llama.cpp Releases
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llama.cpp b12023 fixes shared prefix bug
This release addresses a specific inference artifact where decision prefixes were incorrectly bleeding into output embeddings. By capping shared prefixes at the first output position, llama.cpp ensures that option tokens remain distinct for parent and child tasks. This is a targeted fix for multi-step reasoning or voting workflows rather than a performance boost. It stabilizes complex generation patterns without changing the underlying model architecture.
llama.cpp Releases
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llama.cpp b12024 adds Snapdragon and ROCm 10
This release quietly expands llama.cpp's hardware support to include Qualcomm Snapdragon devices with CPU, Adreno GPU, and Hexagon NPU acceleration. It also updates ROCm to version 10.0 for both Linux and Windows, keeping AMD users in step with the latest driver stack. CUDA builds now default to 13.4 libraries alongside 12.x options, reflecting the industry's gradual shift toward newer NVIDIA toolkits. While no new model architectures are introduced, the binary matrix grows significantly, ensuring local inference remains viable on a wider array of edge and server hardware.
llama.cpp Releases
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