Massive models were too slow to scale. Moving to H100 inference cut latency by 50% and slashed costs by 4x.
Founded by the former engineering lead for PyTorch at Meta, this platform enables developers to run and fine-tune large language models efficiently.
Foundation models with billions of parameters require massive compute resources, making them too slow or expensive for production use. Developers...
“Achieving optimal cost-performance for scale and productionization is a primary challenge for customers developing on PyTorch. It’s particularly true with generative AI products and models because of their sheer size as well as how new and fast this field is. We wanted to use AWS to help to bridge this gap.”
Inference platform for deploying and fine-tuning open-source generative AI models.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Closed models lagged and broke flow. Self-hosting Llama cut latency 3x, letting a single GPU power 1,000 engineers.
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
Testing chunking strategies bottlenecked RAG deployment. A real-time sandbox now validates optimal settings instantly.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.