gpu performance

Feb 18

Triton Language

Jeffrey Wang

The world of GPU programming for AI has come a long way since I worked on writing a CUDA-based matrix library back in 2009. Both NVIDIA hardware and the CUDA ecosystem have evolved dramatically and are now the basis for the majority of AI compute in the world today (hence the $4T+ market cap)....

large language models research paper gpu performance

Feb 08

Quantization-Aware Distillation

Jeffrey Wang

Following up on the last post, I want to write about this new paper from NVIDIA on quantization-aware distillation. We learned about quantization last time, so let's talk about distillation now. Model distillation is the process of transferring knowledge from one model (the teacher) to another,...

machine learning large language models gpu performance

Feb 03

LLM Quantization and NVFP4

Jeffrey Wang

With the rise of large language models and the desire to run them more cheaply and efficiently, the concept of quantization has gained a lot of traction. By representing the weights of the LLM with data types that use fewer bits, you reduce the necessary GPU memory to load the model and the memory...

machine learning large language models gpu performance