Simon Mo
where else to find them
no profile links on file
what they talk about
all 5 in the filters
where they keep going back
- Ray Summit2 editions2020–2021
all 4 in the filters
on a stage with
- DMYTRO DZHULGAKOVonce
- Jana van Greunenonce
- Lianmin Zhengonce
all 6 in the filters
who talks about the same things
- Robert Nishihara11 talks
- Michael Suo6 talks
- Luca Antiga5 talks
- Alejandro Saucedo33 talks
- Tunji Ruwase2 talks
- Aparna Ramani7 talks
| title | conference | date |
|---|---|---|
| vLLM & Deepspeed Updates | PyTorch Conference 2025 | 2025-10-22 |
vLLM presents an inference engine that runs open models on heterogeneous datacenter hardware, integrates PyTorch features like torch.compile and flex… | ||
| vLLM: Easy, Fast, and Cheap LLM Serving for Everyone | PyTorch Conference 2025 | 2025-10-22 |
vLLM serves open‑source LLMs with claims of production scale on NVIDIA/AMD GPUs, TPUs, Trainium and CPUs, plus OpenAI and Anthropic API compatibility and… | ||
| Panel Discussion: Training and Inference at Planet Scale | AI Infra Summit at PyTorch Conference 2025 | 2025-10-21 |
Panel covers concrete engineering moves: using five- or six-dimensional parallelism and mixture-of-experts sharding, splitting prefill vs decode into separate… | ||
| Ray Serve: Patterns of ML Models in Production | Ray Summit 2021 | 2021-06-22 |
Ray Serve is a Ray-based serving framework developed from 100+ user interviews that claims to deploy many models as composable microservices with per-component… | ||
| Patterns of ML Models in Production | PyCon US 2021 | 2021-05-14 |
Provides concrete patterns — pipeline, ensemble, business logic and online learning — backed by 100+ user interviews, names the real pain points (scaling one… | ||
| Introducing Ray Serve: Scalable and Programmable ML Serving Framework | Ray Summit 2020 | 2020-09-30 |
Ray Serve is a distributed Python web framework on Ray that replaces Flask-or-TFServing tradeoffs by letting HTTP APIs own preprocessing/postprocessing while… | ||
| nothing matches | ||