Triton
Beyond Market Intelligence keeps Triton in one place: 5 stories so far. The section currently leads with “From naive GEMM to FlashAttention: a free code-first GPU kernel series”, “Explore how Triton simplifies custom GPU kernels for faster ML workflows.”, and “Beyond the Hype: A Practical Guide to Sparse Attention Claims”. Most GPU kernel tutorials treat optimization as a black art. Triton shines when the bottleneck is memory-bound rather than compute-bound. 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 Triton story on Beyond Market Intelligence, newest first.
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.
Explore how Triton simplifies custom GPU kernels for faster ML workflows.
Triton shines when the bottleneck is memory-bound rather than compute-bound. Operations like element-wise fusions, reductions, or attention that stall on data movement are prime candidates for custom kernels. Harshwardhan Fartale's early-access book gets this right: it focuses on practical acceleration, not theory. If you're stuck waiting on a stubborn layer, that's the signal to explore. For those new to kernel work, pairing this with broader distributed training context, like the guide on LLM training algorithms, helps frame when optimization matters most.
Beyond the Hype: A Practical Guide to Sparse Attention Claims
Reading how a practitioner can make sparse attention look deceptively strong, through careful benchmark selection, unoptimized baselines, and aggregate reporting, is both uncomfortable and necessary. The honest admission that even good-faith researchers fall into these traps makes it more valuable, not less. This isn't about dismissing the field but about demanding rigor where it's easy to cut corners. For anyone navigating efficient attention research, it's a practical warning worth internalizing before your next evaluation.
Netflix's blueprint for scaling LLM inference across diverse hardware
Netflix's move to detail its in-house LLM serving platform is a practical look at the messy, real-world work of scaling AI. Supporting different model sizes, juggling hardware constraints, and keeping pace with fast-moving engines like Triton and vLLM isn't glamorous, but it's essential. The company's willingness to share these production lessons offers a grounded counterpoint to the usual hype. If you're wrestling with similar trade-offs, Netflix's account is worth your time, and our related article on distributed training algorithms pairs well with it.
Explore a new recurrent architecture built for deeper reasoning and memory tasks.
After months of independent work, DABSN (Dynamic Adaptive Bias State Network) is now public, with a preprint and code that includes PyTorch, C++, and Triton implementations. The architecture's behavior on reasoning and long-sequence benchmarks is compelling, but the 24M-parameter language model trained on 1B tokens is where the results get genuinely interesting. Now, the ask is for collaborators: independent reproduction, stronger evaluations, or GPU access to push scaling further. It's a practical, open invitation to build on something promising, not a promise of perfection.