Zero-Copy GPU Inference from WebAssembly on Apple Silicon: Direct Memory Sharing Between Wasm and GPU
A WebAssembly module's linear memory can be shared directly with the Apple Silicon GPU: no copies, no serialization, no intermediate buffers. Here's how the zero-copy chain works, what we measured…
Read the full articleYou might also wanna read
Optimizing LLM Inference: A C++ Backend for VRAM-Aware Sequence Packing
A comprehensive guide to optimizing LLM inference by eliminating padding overhead with hardware-aware sequence packing.
Why Your M4 Max Runs LLMs Like a GPU Beast — Without a Single CUDA Core
The secret isn’t brute-force compute. It’s a memory architecture that rewrites the rules of local AI inference. Continue reading on Medium »
APEX4: Platform-Dependent W4A4 LLM Inference via Intra-SM Compute Rebalancing
W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing system

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
Researchers Serve 229B-Parameter MoE Model Across Five Consumer GPUs Over Public Internet
We serve MiniMax-M2.5, a 229B-parameter mixture-of-experts model, split across five consumer RTX 5090s in five European countries. The stage
Mesh LLM Needs Failure Planning, Not Just Free GPUs, Experts Warn
Mesh LLM, an OpenAI-compatible API capable of running locally or distributing model layers across multiple machines, gained significant trac

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