
Together AI has released Together Compiler (TKCC), an autonomous system that uses AI agents to continuously optimize inference workloads for varying models and hardware configurations. The system achieves up to 252x higher experimentation throughput by using persistent engine instances rather than restarting inference engines for each test, enabling rapid iteration over optimization strategies. TKCC has set new state-of-the-art results on the SemiAnalysis InferenceX benchmark, achieving 1111 TPS on MiniMax M3.1-Flash and 1205 TPS on DeepSeek v4.1-Flash, while outperforming hand-tuned megakernels by 1.7x on Llama 1B. The approach also demonstrates generalizability to non-language domains including weather modeling and partial differential equations.
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Together AI automates inference optimization with agents
2 developments
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13.5 and ROCm 10.0 alongside existing CUDA 12 builds. AMD GPU users finally get first-class parity with NVIDIA, while Snapdragon mobile chips gain dedicated NPU acceleration paths. Apple Silicon KleidiAI is explicitly disabled here, a notable regression for Mac performance that suggests ongoing tuning issues. The inclusion of OpenVINO and SYCL backends further cements llama.cpp as the universal runtime for inference across nearly every major silicon architecture.
Kimi’s new K3.1-Pro model joins the llama.cpp ecosystem, bringing a distinct architectural twist to local inference. By replacing full-attention MLA layers with DSA (Dynamic Sparse Attention) and linear-attention KDA layers, this release tests how well sparse caching handles hybrid attention patterns. The implementation keeps indexer precision high to avoid quality loss during top-k selection, ensuring the model runs accurately on consumer hardware. This expands the library’s reach into newer Chinese open-weight models that rely on complex memory management.
This release quietly cements llama.cpp as the universal inference runtime by adding default builds for CUDA 13.5 and ROCm 10.0 across Linux and Windows. The inclusion of ROCm 10 means AMD GPU users no longer need custom compilation to access modern acceleration, effectively closing the gap with NVIDIA's ecosystem support. While KleidiAI on Apple Silicon is disabled in this build, the broader platform coverage ensures that local inference remains accessible on virtually any hardware configuration without manual setup.