hgpu.org » nVidia RTX PRO 6000
Musa Cim, Burak Topcu, Mahmut Taylan Kandemir
March 15, 2026 by hgpu
Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong, Caiwen Ding
Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, LLM, nVidia, nVidia H200, nVidia RTX PRO 6000
March 4, 2026 by hgpu
Recent source codes
* * *
Most viewed papers (last 30 days)
- Architecture-Aware LLM Inference Optimization on AMD Instinct GPUs: A Comprehensive Benchmark and Deployment Study
- AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search
- LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs
- CuTeGen: An LLM-Based Agentic Framework for Generation and Optimization of High-Performance GPU Kernels using CuTe
- DRTriton: Large-Scale Synthetic Data Reinforcement Learning for Triton Kernel Generation
- MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?
- Mixed-precision numerics in scientific applications: survey and perspectives
- Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context
- SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits
- MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
* * *



