CUDA
CUDA at Beyond Market Intelligence is a file of 11 stories. The newest of them: “Cross-platform AI compression that stays consistent across any hardware”, “From naive GEMM to FlashAttention: a free code-first GPU kernel series”, and “From telecom to AI: navigating the next step in your research journey”. One hash across seven backends proves that integer-only inference can solve the cross-platform compression problem that has long plagued LLM deployment. 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 CUDA story on Beyond Market Intelligence, newest first.
Cross-platform AI compression that stays consistent across any hardware
One hash across seven backends proves that integer-only inference can solve the cross-platform compression problem that has long plagued LLM deployment. When float GPT-2 encoded on a Mac decoded zero of four windows on x86, the root cause was clear: floating-point inconsistency across hardware. This project's solution, int8 weights, fixed rounding, and lookup tables, delivers exact reproducibility without sacrificing performance. For deeper context on building efficient AI systems, our "Building a GPT-2 KV Cache" piece explores similar performance measurement challenges.
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.
From telecom to AI: navigating the next step in your research journey
Four hundred cold emails. One hundred applications. Zero traction. This researcher has the technical depth, RL, LLMs, post-training, even CUDA, but the walls are real: location and university brand are blocking doors that merit should open. A second Master's abroad is one path, but it's not the only one. Explore industry roles where demonstrated output outweighs pedigree, or target labs that value remote collaboration. The signal is clear: adapt the approach, not the ambition.
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.
Nvidia sees 70% growth ahead as AI spreads across every industry
Jensen Huang sees another year of plenty ahead, projecting 70% growth for Nvidia. That confidence stems from reach: the company has its finger in every pie, from chips to software to AI infrastructure. He insists the deals driving this aren't circular, a claim worth probing given the complexity of the ecosystem. It's a bold forecast, but Huang's track record gives it weight. For context on how such sprawling systems operate, our guide to distributed training algorithms offers a grounded starting point.
Discover How Predictive Coding Networks Achieve Near-Perfect Accuracy at Speed
Backpropagation has dominated deep learning for decades, but Deepity makes a compelling case that it's not the only path forward. This C++ library's Predictive Coding Networks hit 97.73% on MNIST in under a minute, edging close to PyTorch's 98.27% while offering biological plausibility and continual learning potential. The trick lies in algorithmic caching and recent feedback alignment research, closing the performance gap on CPU.
Unlock faster local LLM inference with DSpark speculative decoding
Waiting for the local model to generate its next token can feel like watching paint dry. DSpark speculative decoding changes that. By using a smaller draft model to propose candidates and the larger Qwen3-8B to verify them, it accelerates generation on the same GPU. We appreciate that this works directly with llama.cpp and CUDA, making it practical for your own hardware. It is a straightforward approach to reclaiming speed. For more on the broader concepts, our guide to distributed training is a helpful companion.
When Milliseconds Matter: Teaching LLMs to Forget on Purpose
Most LLM runtimes treat memory as an endless resource, but this one treats it as a deadline. It refuses admission rather than miss a 33ms robot control cycle, evicting KV cache by meaning instead of age, all in hand-written CUDA. That's a sharp rebuke to the bloat most of us accept. It's a focused, practical stand for precision, and it makes you wonder what else we're letting slip.
Catch costly PyTorch bugs before they waste your GPU hours
Torch-preflight is the kind of tool PyTorch developers didn't know they needed until they saw it. The author spent years watching simple mistakes, like forgetting `zero_grad()` or holding onto autograd graphs, burn GPU hours. Instead of waiting for failures, this linter reads your code without importing or executing it, catching those bugs upfront. With 13 rules and VRAM estimation that lands within 4% of measured peaks, it's practical and honest.
Build Your Own LLM Runtime with a Guided Tour of Real Bugs
There's a particular satisfaction in packing your own weights and capturing your own CUDA graphs, and that's exactly the terrain this walkthrough covers. The post traces the building of annotated-llm-runtime on an H100, with three bugs generating most of the hard-won annotations. It's a practical, grounded tour for anyone curious about the mechanics beneath the abstraction. For broader context on the distributed side, *Unlock LLM Training* pairs well here, though this piece stands firmly on its own.
Why Your AI Model Slows 170x on a T4 vs A100
A 170x slowdown between a T4 and an A100 is not a generational gap; it's a signal that something structural is off. For a point-tracking model with dense 4D correlation volumes and transformer layers in pure FP32, you're likely hitting the T4's lack of Tensor Core support for your exact ops, forcing a fallback to inefficient kernels. But that alone shouldn't explain the magnitude. Profile kernel-level timing first. Look for operations like `grid_sample` or `einsum` that might be serializing on the T4's memory bandwidth.