Achieving 262k Context Length on Consumer GPU with Triton/CUDA Optimization
Published:Dec 27, 2025 15:18
•1 min read
•r/learnmachinelearning
Analysis
This post highlights an individual's success in optimizing memory usage for large language models, achieving a 262k context length on a consumer-grade GPU (potentially an RTX 5090). The project, HSPMN v2.1, decouples memory from compute using FlexAttention and custom Triton kernels. The author seeks feedback on their kernel implementation, indicating a desire for community input on low-level optimization techniques. This is significant because it demonstrates the potential for running large models on accessible hardware, potentially democratizing access to advanced AI capabilities. The post also underscores the importance of community collaboration in advancing AI research and development.
Key Takeaways
Reference
“I've been trying to decouple memory from compute to prep for the Blackwell/RTX 5090 architecture. Surprisingly, I managed to get it running with 262k context on just ~12GB VRAM and 1.41M tok/s throughput.”