The llama.cpp project has released a new update featuring the Kimi-K3 text model. This model enhances the existing Kimi-Linear-48B architecture with new features such as cross-layer residual attention and a situ activation function. Additionally, it includes a full-rank KDA gate and an MLA output gate, which are designed to improve model performance. These advancements provide developers with more robust tools for text generation, marking a notable progression in AI model capabilities.
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llama.cpp Releases · July 2, 2026 · Same story
llama.cpp Releases · November 7, 2026 · Same story
Kimi-K3 Text Model Added to llama.cpp
3 developments
This release targets the persistent performance gap on AMD hardware by optimizing Vulkan Flash Attention specifically for RDNA3 and RDNA4 architectures. The changes include reducing shared memory usage through buffer reuse and fixing critical stability issues like NVIDIA hangs and sparse FA underflow bugs. While there are no new model architectures, these low-level shader optimizations directly translate to faster inference speeds for local users on modern AMD GPUs. It is a quiet but necessary engineering update that tightens the ecosystem for non-NVIDIA accelerators.
This release quietly extends llama.cpp’s hardware support to the latest NVIDIA and AMD stacks. By shipping binaries for CUDA 13.5 and ROCm 10.0, it ensures users on cutting-edge GPUs don't have to compile from source to get performance gains. The Vulkan build also fixes header inclusion issues that previously broke compilation on some Linux distributions. It’s a maintenance update rather than a feature leap, but it keeps the local inference runtime compatible with the fastest consumer and datacenter hardware available today.
This is a routine maintenance release for llama.cpp that quietly extends hardware support to the latest NVIDIA and AMD stacks. By adding CUDA 13.5 and ROCm 10.0 binaries, it ensures local inference remains viable on cutting-edge GPUs without waiting for community forks. The build also includes OpenVINO and SYCL variants, reinforcing its role as the universal runtime for diverse accelerators. While no new model architectures are introduced, this update keeps the toolchain compatible with modern driver ecosystems.
© GitHub ChangelogGoogle’s latest Flash model is now generally available in GitHub Copilot, targeting agentic coding and multistep workflows. It offers improved instruction following over Gemini 3.8 Flash and is billed at provider list pricing under usage-based billing. The rollout is gradual across Visual Studio Code, JetBrains IDEs, and other platforms. Administrators can manage access through model policies in Copilot settings.
© TechCrunch AIElevenLabs is moving beyond voice synthesis into the crowded meeting intelligence space with ElevenAI. The app merges transcription, dictation across 90+ languages, and agent-driven follow-ups like drafting emails directly from conversation context. This positions their $22B valuation engine as a central knowledge hub for enterprises, aiming to replace standalone notetakers. It’s a strategic play to lock teams into their ecosystem using the recently launched v3 Scribe models.
© TechCrunch AIGoogle is quietly shifting its search infrastructure by replacing older fast models with Gemini 4 Hydrogen, a tier designed specifically for speed and cost-efficiency rather than raw reasoning power. This model completes tasks 2.5 times faster than its predecessor while beating the more powerful Argon on specific intelligence benchmarks, proving that specialized efficiency can outperform brute force in high-volume workflows. By integrating this into Search's fan-out technique, Google now breaks down complex queries into subqueries with greater precision and speed, delivering richer visual responses without the latency of heavier models. This marks a strategic pivot toward agentic execution layers within consumer search, making AI Mode feel snappier for everyday multi-part questions.