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.