Elias Frantar
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolElias Frantar is a researcher and Member of Technical Staff at OpenAI with nine years of experience making massive ML models far more efficient. He completed a PhD at IST Austria under Dan Alistarh, where he created breakthrough compression tools—GPTQ and SparseGPT—for low-bit quantization and sparsification of 100B+ models and built Marlin, an INT4xFP16 inference kernel achieving near-ideal speedups. During internships at Google Brain and DeepMind he characterized scaling laws for sparse foundation models and developed QMoE to compress and run trillion-parameter Mixture-of-Experts models in resource-constrained settings. His work spans theory, algorithm design, and highly optimized CUDA kernels, delivering state-of-the-art speed-vs-accuracy trade-offs across vision and language models. Based in San Francisco, he pairs deep research with hands-on engineering and unusual hobbyist chops—designing record-setting, super-fast Lego Rubik’s Cube robots that have attracted millions of views.
9 years of coding experience
2 years of employment as a software developer
Vienna University of TechnologyDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Institute of Science and Technology AustriaHigh School Diploma, Informatics, GPA: 4.00/4.00, High School Diploma, Informatics, GPA: 4.00/4.00 at Technologisches Gewerbemuseum
Github Skills (38)
pytorch10
llama10
inference10
tpu10
amd10
llm10
robot10
llmops10
cuda10
transformer10
gpt10
high-speed10
robotics9
xpu9
mlops9
Programming languages (2)
Github contributions (5)
efrantar/mirrcub3r
May 2018 - Feb 2020
Contributions:114 commits, 94 pushes, 1 branch in 1 year 8 months
IST-DASLab/spdy
Jul 2022 - Oct 2022
Code for ICML 2022 paper "SPDY: Accurate Pruning with Speedup Guarantees"
Contributions:1 review, 4 commits, 1 PR in 3 months
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