Model Releases
Java vllm-like framework claims 90% of perfomance of llama.cpp on local inference on NVIDIA GPUs by compiling Java to CUDA and cuTile
TornadoVM: The Java to CUDA engine: https://github.com/beehive-lab/TornadoVM jitLLM: The inference engine: https://github.com/beehive-lab/jitllm Deep dive talk: https://www.youtube.com/watch?v=HO5CpET
TornadoVM: The Java to CUDA engine: https://github.com/beehive-lab/TornadoVM jitLLM: The inference engine: https://github.com/beehive-lab/jitllm Deep dive talk: https://www.youtube.com/watch?v=HO5CpETzywk submitted by /u/mikebmx1 [link] [comments]
Related
- Benchmarks: TensorSharp vs. llama.cpp
- Swift 1.5 on veloGB10, ~110 tok/s on 2× DGX Spark: xhigh beats base Flash-Next at medium on vLLM
- NInfer vs llama.cpp vs vLLM: quality + speed comparison for Qwen3.8-27B NVFP4 on RTX 5090
- How well do multiple GPUs scale for LLM inference? (Trying to understand the basics)
Source: r/LocalLLaMA | 2026-10-08