FP8
3 stories filed under FP8 on Beyond Market Intelligence. The newest of them: “Navigating CUDA 13 setup: GPU limits, timing traps, and profiler access”, “From naive GEMM to FlashAttention: a free code-first GPU kernel series”, and “Qwen3.8-27B brings powerful local AI agents to your own machine”. GPU kernel engineering is full of traps that waste hours, and the CUDA 13 setup session from this free video series collects the ones that cost most. Most GPU kernel tutorials treat optimization as a black art. 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 FP8 story on Beyond Market Intelligence, newest first.
Navigating CUDA 13 setup: GPU limits, timing traps, and profiler access
GPU kernel engineering is full of traps that waste hours, and the CUDA 13 setup session from this free video series collects the ones that cost most. Check compute capability, not just "has an NVIDIA GPU", Turing, Ampere, Ada, and Hopper each unlock different features. Driver, toolkit, and PyTorch's runtime are three separate things. GPU launches are asynchronous, so naive timing measures queueing, not work. A tiny smoke test before anything else saves the day.
From naive GEMM to FlashAttention: a free code-first GPU kernel series
Most GPU kernel tutorials treat optimization as a black art. This series opens it up. The insight that performance is a data-movement problem first, compute second, reframes everything. Building FlashAttention from scratch, where tiling avoids writing the full attention matrix to memory, is a perfect worked example. The 44-session roadmap covers GEMM through Hopper architecture, with live profiling at every step. If you write production kernels, this is the kind of resource worth contributing feedback to.
Qwen3.8-27B brings powerful local AI agents to your own machine
The 27-billion-parameter model from Alibaba landed quietly on Hugging Face, but developers heard it loud and clear. Qwen3.8-27B isn't just another compact local model; it brings coding agents, image understanding, and a 262,144-token context window to hardware you might already own. Third-party tests now match it with cloud-only systems from months ago. That gap is closing faster than expected, and for enterprises watching data privacy and cost, the appeal is obvious. It's worth exploring how this changes what "local" really means.