CUDA kernels
CUDA kernels at Beyond Market Intelligence is a file of 2 stories. The newest of them: “When AI Writes Faster CUDA Kernels Than PyTorch, Benchmarks Decide” and “Distributed AI inference across clouds with smarter latency handling”. AI agents are now writing CUDA kernels that beat PyTorch on speed, but proving those gains are real is the harder problem. Pushing 28 TPS on Qwen2.5-7B across two cloud regions over public WAN is a concrete milestone, and the trick isn't faster hardware, it's smarter batching. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every CUDA kernels story on Beyond Market Intelligence, newest first.
When AI Writes Faster CUDA Kernels Than PyTorch, Benchmarks Decide
AI agents are now writing CUDA kernels that beat PyTorch on speed, but proving those gains are real is the harder problem. I tested them on an NVIDIA DGX Spark and found that benchmark design matters as much as the code itself. When the hype machine sells you AI at every turn, a clear-eyed test like this cuts through the noise. For more on staying grounded, see our related piece on keeping your judgment amid the AI sales pitch.
Distributed AI inference across clouds with smarter latency handling
Pushing 28 TPS on Qwen2.5-7B across two cloud regions over public WAN is a concrete milestone, and the trick isn't faster hardware, it's smarter batching. By treating WAN latency as a per-round cost rather than a per-token penalty, ShardFlow's speculative decoding with K=8 drafting commits 4 tokens per round trip instead of one. That's the kind of practical insight that makes distributed inference feel less like a science project and more like a real tool.