Inference (@inference

Inference

376 posts

Inference

@inference_net

Inference infrastructure for AI-native teams

San Francisco, CA

Joined March 2024

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Inference
@inference_net
Apr 14
Catalyst is live built for teams shipping agents in production train & deploy frontier LLMs in minutes, using the data your application is already generating Get Started: docs.inference.net/introduction
Sam Hogan 🇺🇸
@samhogan
Apr 14
Introducing Catalyst: a developer platform to monitor, train & deploy self-improving AI models built for teams operating AI products at scale Catalyst can automatically: - collect traces from your agents - curate training data & evals - train & deploy models on par w/ Opus 4.6
Inference
@inference_net
Jun 10
Specialized models are becoming a practical path to better AI UX. Olive moved from a frontier model to a custom model trained with Inference Catalyst for their food verdict workflow. After a user scans a product, the model now delivers near-instant verdicts on what to watch out
How Olive Delivers Real-Time Food Verdicts on a Model It Owns | Inference.net
From inference.net
Inference
@inference_net
May 19
The best production model is the one trained for the job. Gravity Ads replaced a 70B model on Cerebras with a specialized 1B model trained for their actual workload. Same quality, much faster and cheaper inference: - p50: 152ms - p99: 5.7x lower - cost: ~10x lower - model: 70x
How Gravity Ads Trains Specialized LLMs to Power Their AI-Native Ad Network | Inference.net
From inference.net
Inference
@inference_net
Mar 11
Day Zero fine-tuning & hosting support for Nemotron 3 Super by @nvidia is now live Fine-tune on real production traces & deploy on high-performance infrastructure optimized for Nemotron 3 Super Your data, your weights, your performance edge Learn more:
How Inference.net trains Specialized Language Models that cut AI costs by up to 50x | Inference.net
From inference.net
Inference
@inference_net
Feb 13
We built the Kimi K2 of web extraction: meet Schematron. It's been getting a lot of love from teams we work with. This is what we heard again this week: "We tested Schematron against smaller models for large-scale HTML schema extraction — it was more accurate and significantly
Schematron: An LLM trained for HTML -> JSON at scale | Inference.net
From inference.net