llama.cpp Q4_K Prefill Fusion: Gate, Up, and SwiGLU in One CUDA Path
A new llama.cpp CUDA PR targets duplicate activation quantization and intermediate FP32 traffic in dense Q4_K MLP prefill, with contributor-reported gains from 4.1% to 14.7%.
The RAMGPT library / 3 articles
Choose CPUs, GPUs, memory, and storage for AI workloads.
A new llama.cpp CUDA PR targets duplicate activation quantization and intermediate FP32 traffic in dense Q4_K MLP prefill, with contributor-reported gains from 4.1% to 14.7%.
A new llama.cpp CUDA patch shows that Blackwell NVFP4 prefill was limited by tile delivery, barriers, and register pressure—not simply tensor-core arithmetic.
Learn how to estimate LLM VRAM requirements from model size, quantization, KV cache, context length, runtime overhead, and GPU offloading.