TensorRT Doubles Protein Prediction Speed
AllThingsApx · x · 2026-07-11
NVIDIA shared a case study from the University of Washington's protein design team: they used cuEquivariance and TensorRT to accelerate the structural prediction model RF3. For a 256-residue protein, inference time was reduced from 5.8 seconds to 2.5 seconds, achieving over 2× speedup.
The core takeaway here isn't a model release, but rather:
- Geometric core computations were successfully accelerated.
- The NVIDIA toolchain can significantly reduce wait times in protein design workflows.
- This has direct implications for scientific workflows like protein design and structural prediction.
More from Infra
- LLM Serving Metrics Thread: Why TPOT and Uptime Make or Break User Experience — abhijithneil · 2026-09-11
- PlanetScale launches sharded Postgres: 768 servers acting as one, 1PB scale — dhruv2038 · 2026-09-11
- Can a 7900 XTX 24GB run Qwen locally? Reddit seeks ROCm tok/s benchmarks — thenomadexplorerlife · 2026-09-11
- RTK Terminal Compression Cuts Tokens but Leaves Your AI Coding Bill Unchanged — Bartaseth · 2026-09-11
- SF Compute founder: buying compute is 'an absolutely awful experience' right now — IgorCarron · 2026-09-11
- SmolVM open-sources persistent computer infrastructure for agents that outlive chat sessions — aniketmaurya · 2026-09-11