June 2026 monthly summary: Delivered substantial RVV (RISC-V Vector) quantization performance enhancements for quantized models in ggml-org projects. Implemented 512/1024-bit VLEN support in RVV vector dot product paths for ggml-cpu and the llama.cpp integration, enabling higher throughput and lower latency for quantized inference. Introduced 512/1024-bit implementations across iq4_xs, q6_K, i-quants, and extended coverage to tq3_s, iq3_xxs, iq2_s, iq2_xs, iq2_xxs, with refactors to support the new VLENs. Coordinated across repositories ggml-org/ggml and ggml-org/llama.cpp with co-authored contributions. This work improves performance-per-dollar for quantized workloads and positions the project to accelerate experimentation with larger VLEN configurations.

Performance-driven monthly summary for 2026-04 focusing on RISC-V RVV vectorization optimizations across ggml and llama.cpp, delivering a SIMD GEMM kernel and 128-bit RVV implementations for quantization vector dots. Highlights include cross-repo collaboration and RVV compatibility refactors to maximize hardware performance and compatibility on RVV-enabled devices.

March 2026 performance-focused feature delivery across ggml and llama.cpp: RVV-accelerated quantized dot-product kernels, codebase refactors, and alignment of quantization paths to boost CPU inference throughput on quantized workloads.