CUDA
CUDA on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on cuda in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around cuda, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Deepity: A C++ library showing Predictive Coding Networks can match Backprop (97.73% on MNIST in 60s) [P]
Deepity, a newly developed C++ library, demonstrates the potential of Predictive Coding Networks (PCNs) to rival established backpropagation methods in machine learning. Through innovative algorithmic caching and incorporating recent research on Direct Kolen-Pollack Feedback Alignment, Deepity achieves 97.73% test accuracy on MNIST within 59.5 seconds – remarkably close to PyTorch’s 98.27% in 70 seconds. This significant performance leap addresses a historical challenge with PCN implementations.
Speed Up LLM Inference with DSpark Speculative Decoding
Accelerate your local LLM generation speed with DSpark speculative decoding. This technique leverages your existing GPU to significantly boost performance, demonstrated here with Qwen3-8B, llama.cpp, and CUDA. DSpark intelligently predicts upcoming tokens, minimizing computation and maximizing throughput. Explore this transformative approach to AI inference and unlock greater efficiency. For a broader perspective on the shift toward local AI, see our article, "Apple's New Mac Line is Built Around Local AI." Discover how to harness this power today.
Can an LLM Forget the Right Things?
Large Language Models (LLMs) often operate without awareness of real-time constraints, a limitation this innovative runtime directly addresses. Unlike typical inference systems, it prioritizes timely execution – refusing to run if it risks missing critical deadlines, like controlling a robot. This architecture, entirely hand-written in CUDA, intelligently manages its KV cache by meaning, not just age. Explore the details in "Can an LLM Forget the Right Things?" and delve deeper into enterprise applications with "10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong."
A linter for PyTorch 'torch-preflight' [P]
Avoid costly GPU errors and optimize your PyTorch training runs with `torch-preflight`, a new linter designed to catch common pitfalls. Developed over months of experience, this tool analyzes your code – without execution or GPU requirements – to identify issues like unmanaged autograd graphs or improper gradient accumulation. Beyond bug detection, `torch-preflight` estimates VRAM usage, providing actionable insights to ensure your training fits within available resources. Explore the project and contribute at [https://github.com/highwaterlabs/torch-preflight](https://github.com/highwaterlabs/torch-preflight). For more on model evaluation, see our related article, "
How To Build Your Own LLM Runtime From Scratch
Ever wondered what it takes to build an LLM inference runtime from the ground up? This comprehensive guide details that journey, walking you through the creation of a small runtime called annotated-llm-runtime, all while running on an H100. We explore the intricacies of managing weights and CUDA graphs, highlighting three key bugs that shaped the development process. Delve into the complexities of AI infrastructure—as explored further in "OpenAI’s AI spending spree has ballooned to $750B"—and empower yourself with a deeper understanding of LLM technology.
PyTorch model running 170x slower on T4 vs A100. What could cause a bottleneck this extreme? [D]
A recent report highlights a stark performance disparity: a PyTorch model experienced a 170x slowdown when running on an NVIDIA T4 versus an A100 GPU. This extreme bottleneck, observed with a point-tracking model processing 47 frames at 256x256 resolution, suggests factors beyond typical generational hardware differences. With 99% GPU utilization and pure FP32 precision, potential causes include inefficient 4D correlation volume calculations or transformer layer performance. Further profiling is recommended to pinpoint the specific bottleneck.