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LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

Erik Schultheis, Dan Alistarh

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Tags: Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4090, nVidia GeForce RTX 5060 Ti, nVidia L40s, Package

March 22, 2026 by hgpu

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

Kris Shengjun Dong, Sahil Modi, Dima Nikiforov, Sana Damani, Edward Lin, Siva Kumar Sastry Hari, Christos Kozyrakis

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, nVidia H100, nVidia L40s, nVidia RTX A6000, Performance

February 23, 2026 by hgpu

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Yaoyao Ding, Bohan Hou, Xiao Zhang, Allan Lin, Tianqi Chen, Cody Hao Yu, Yida Wang, Gennady Pekhimenko

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia H100, nVidia L40s, Package, Programming Languages, PTX, Triton

December 29, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Bertil Schmidt, Felix Kallenborn, Alexander Wichmann, Alejandro Chacon, Christian Hundt

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Tags: Bioinformatics, Biology, Computer science, CUDA, FPGA, Genomics, nVidia, nVidia A100, nVidia H100, nVidia L4, nVidia L40s, nVidia V100, Package

November 24, 2024 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

KernelBench: Can LLMs Write Efficient GPU Kernels?

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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