TPDE-LLVM Open Source Project Achieves 10-20x Faster LLVM -O0 Back-End Performance
5 years ago, @nikic wrote: I can’t say a 10% improvement is making LLVM fast again, we would need a 10x improvement for it to deserve that label. We recently open-sourced TPDE and our fast LLVM…
Read the full articleYou might also wanna read
vLLM transformers backend now matches custom implementation speeds for LLM inference
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Why Real-Time LLM Performance Still Hits a Wall Despite Faster GPUs
Benchmark gains often mask a deeper challenge: memory-bound token generation remains LLM inference's limiting factor. The post Why Real-Time
RTP-LLM: Alibaba's High-Performance Inference Engine for Large Language Model Deployment
Large Language Models (LLMs) have revolutionized AI applications, but deploying them at scale presents significant challenges. We present RT

I ran 133 benchmarks to find out if vLLM is actually faster than HuggingFace
Spoiler: it depends on what “faster” means to you If you’ve ever tried to serve a large language model in production, you’ve probably come a
Developer Claims 9.9x Lower TTFT on Android by Reusing llama.cpp KV State
A developer has reported achieving a 9.9x reduction in time-to-first-token (TTFT) for local large language model inference on a real Android

LLM Inference Benchmarking - Measure What Matters
Production-grade LLM inference is a complex systems challenge, requiring deep co-designs - from hardware primitives (FLOPs, memory bandwidth

Comments
Sign in to join the conversation.
No comments yet. Be the first.