The llama.cpp project released version b10840, which introduces performance optimizations for CUDA inference. The update implements branchless computation for Q4_K and Q5_K quantization formats to accelerate matrix-vector multiplication (mmvq).
- Branchless unpacking prevents scale re-execution for every column in mmvq, improving performance at batch sizes greater than 1.
- L2 prefetch logic is gated specifically for DGX Spark hardware to ensure gains are realized.
- The mmvq L2 prefetch guard is extended to support MUSA and HIP backends alongside CUDA.
- Switch points for Q4_K are updated to accommodate a wider range of models.
These changes provide measurable performance improvements for users running llama.cpp on DGX Spark systems with batch sizes exceeding one.