🚀 RELEASES & LAUNCHES
KoboldCpp adds a lightweight built-in coding-agent harness
KoboldCpp added an integrated agent harness with nine built-in tools.- KoboldCpp users can enable the harness for basic local coding and editing tasks.
- The v1.122 release adds nine built-in tools, a roughly 2,000-token system prompt, MCP support, and OpenAI-compatible backends.
- The agent requires at least 28,000 context tokens and 8,000 generation tokens, with 12GB of VRAM recommended.
- Users can activate it from the GUI Admin tab or with the --agent launch flag, reducing reliance on external agent tools.
BeeNara is a 332MB offline model for document sorting with abstention
BeeNara released a 332MB CPU-friendly model that sorts documents or says none fits.- BeeNara targets users sorting local German- and English-language invoices, letters, contracts and statements into custom folders.
- The 332MB ONNX cross-encoder runs offline without a GPU or PyTorch at about 0.2–0.3 seconds per document.
- It recognizes missing categories in 96.8% of benchmark cases and automatically sorts 64.1% of documents with 99.6% accuracy.
- The model uses calibrated abstention to send uncertain documents to a user instead of guessing.
🔬 RESEARCH
- llama.cpp merges tiled k-quant CPU matmul for up to 7x faster prefills
llama.cpp merged tiled k-quant CPU matmul support using VNNI.- The change targets llama.cpp users running quantized models on x86 CPUs, especially prompt-prefill workloads.
- It processes k-quants in 256×256 int8 tiles with a 16×16 microkernel and VNNI support.
- Benchmarks on an AMD 9950X3D showed 3.55–7.04× the stock throughput for 4,096×4,096 and 8,192×8,192 matmuls.
- The optimization reaches break-even at 32 output rows and falls to 80% of stock performance for pure GEMV.
- The pull request was merged into llama.cpp master on September 26, 2026.
Sources 📎 Coverage: github.com · 👁 via r/LocalLLaMA