#CUDA Tile
44 related articles
Tutorials·1 min
NVIDIA CUDA Tile: A Detailed Look at the New Tile-Based GPU Programming Paradigm
In-depth analysis of NVIDIA's new CUDA Tile programming model: how tile-based GPU kernel development integrates with C++, optimizes data locality, and accelerates Tensor Core computation for HPC, deep learning, and AI inference.
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·3 min
Moonshot AI Open-Sources FlashKDA: A Deep Dive into High-Performance CUDA Kernels for KDA Attention
Moonshot AI open-sources FlashKDA, providing high-performance CUDA kernels for Kimi Delta Attention. Learn about its technical principles, performance gains, and value for long-context training and inference acceleration.
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·3 min
Moonshot AI Open-Sources FlashKDA: A Deep Dive into High-Performance CUDA Kernels for KDA Attention
Moonshot AI open-sources FlashKDA, providing high-performance CUDA kernels for Kimi Delta Attention. Explore its technical principles, performance gains, and value for long-context training and inference.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
A deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on an RTX 5080.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
Deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on RTX 5080. Exploring its architecture, Metal porting plans, and applications in VTuber, AR/VR, and sports analysis.
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·3 min
Ideogram 4.0 Turbo Workflow: An AI Image Generation Solution Balancing Speed and Quality
Detailed look at the Ideogram 4.0 mixed turbo workflow: RTX 4090 inference in just 15 seconds, rivaling Krea2 speed, with stable output up to 8K resolution.
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·3 min
Ideogram 4.0 Turbo Workflow: An AI Image Generation Solution Balancing Speed and Quality
Detailed look at the Ideogram 4.0 mixed turbo workflow: RTX 4090 tested at just 15s inference, matching Krea2 speed with up to 8K resolution output.
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·5 min
DeepSeek's Five-Step Ladder to AGI: A Restrained Roadmap Built on 20,000 GPUs
DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap—from chain-of-thought to embodied intelligence—under a 20,000-GPU constraint, using the TileLang compiler to break domestic substitution challenges while API cash flow backs AGI exploration.
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·5 min
DeepSeek's Five-Step Ladder to AGI: A Restrained Roadmap Built on Twenty Thousand GPUs
DeepSeek founder Liang Wenfeng reveals a five-step AGI roadmap from chain-of-thought to embodied intelligence. How does TileLang crack domestic GPU substitution under a 20,000-card constraint?
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·5 min
Inside Liang Wenfeng's Closed-Door Meeting: DeepSeek's Philosophy of Restraint and Its AGI Strategy
A full breakdown of DeepSeek founder Liang Wenfeng's 4-hour closed-door meeting: no KPIs, only reasonable profits, open-sourcing top models, and tackling AGI via continual learning—a rare AI strategy confession showing how restraint becomes a core edge.
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·5 min
The DeepSeek Investor Q&A with Liang Wenfeng: Decoding Restrained Pricing and the AGI Roadmap
Decoding DeepSeek's Liang Wenfeng 4-hour investor Q&A: 10-month-payback restrained pricing, why open source doesn't hurt revenue, the Agent-continual learning-self-iteration AGI roadmap, plus domestic chips, talent, and your moat.
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·5 min
DeepSeek's Liang Wenfeng Investor Meeting: Restrained Pricing and the AGI Roadmap Explained
An analysis of DeepSeek's Liang Wenfeng 4-hour investor meeting: restrained pricing with 10-month payback, why open source doesn't hurt revenue, the Agent–continual learning–self-iteration AGI roadmap, plus domestic chips, talent, and your moat.
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·5 min
Liang Wenfeng's In-Depth Confession: DeepSeek's Philosophy of Restraint, Open-Source Strategy, and AGI Roadmap
DeepSeek founder Liang Wenfeng systematically explains: a KPI-free culture, 10-month break-even pricing, long-term open-source strategy, continual learning to break the AGI bottleneck, and his prediction of a mature domestic chip ecosystem within a year.
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·6 min
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·6 min
AI Application Engineer Interview Guide: Core Topics on Model Quantization and Inference Deployment
A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·3 min
Bernini Tile-Based Generation: An Engineered Approach to Video Super-Resolution in ComfyUI
Bernini is a ComfyUI video super-resolution node package using Tile Split/Select/Merge to solve seams, drift, and VRAM overflow. Benchmarked at 325s for 39 frames at 1920×1080.
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Handwritten C/CUDA Inference Engine: P…
·2 min
Handwritten C/CUDA Inference Engine: Pushing Qwen 35B to the Limits on RTX 5090
A deep dive into a handwritten C/CUDA inference engine for Qwen 35B on RTX 5090 (Blackwell), covering quantization, FlashAttention kernels, and memory optimization.
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CUDA 13.3 Adds Carryless Multiplicatio…
·2 min
CUDA 13.3 Adds Carryless Multiplication: A Key Step Forward for GPU Cryptography Acceleration
CUDA 13.3 introduces native carryless multiplication support, closing a 15-year GPU gap in AES-GCM, CRC, and cryptographic acceleration. Here's what it means.
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·2 min
ExLlamaV3 v1.0.0 Official Release: Faster Inference, Lossless KV Quantization, and Streamlined Dependencies
ExLlamaV3 v1.0.0 releases with lossless KV cache quantization via kernel fusion, removal of flash-attention-2/xformers, major GEMM/GEMV gains, and broader tensor parallelism support.
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·3 min
GPU Rental vs. Buying: How Low-Usage Teams Should Actually Do the Math
Should low-usage teams rent or buy GPUs? Based on real billing data, we break down hardware costs, electricity, and hidden time losses to help small teams decide.
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