Fireworks AI
Generative AI infrastructure
Massive models were too slow to scale. Moving to H100 inference cut latency by 50% and slashed costs by 4x.
- 2x completion acceptance for Sourcegraph Cody users
Standard inference stalled at 1k tokens/sec. A custom engine hit 10k/sec, cutting 20-second refactors to under 400ms.
An AI infrastructure provider develops specialized small language models to power coding agents for large-scale enterprise environments.
Standard inference engines could not properly allocate memory bandwidth for concurrent users, capping performance at 1,000 tokens per second....
“AWS is infrastructure I can trust. I know AWS is going to be around—AWS has tried-and-tested solutions, and I’m not going to encounter hardware failures or edge cases with memory sharing.”
Developer tools and SDKs for building high-performance AI coding agents.
Cloud computing platform and on-demand infrastructure services.
Related implementations across industries and use cases
Massive models were too slow to scale. Moving to H100 inference cut latency by 50% and slashed costs by 4x.
Manual prompt tuning couldn't keep pace. Automated feedback loops now refine models using real-time user comments.
Closed models lagged and broke flow. Self-hosting Llama cut latency 3x, letting a single GPU power 1,000 engineers.
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.