In the news
ThunderAgent: 2x Faster Agentic Inference for Synthetic Data Generation at Scale
Together AI · Published · 3 min read
In 30 seconds
- What happened
- Together AI released ThunderAgent, a scheduling system achieving 2.5x single-node throughput and 2.4x multi-node speedup for agentic LLM inference by tracking workflows as programs instead of individual requests.
- Why it matters
- Teams running multi-turn agent workloads at scale, especially synthetic data generation pipelines where agents pause for tool calls and resume repeatedly.
- Watch out
- ThunderAgent is a scheduling layer requiring integration with existing inference backends; real-world speedups depend on workload characteristics and whether KV cache thrashing is actually the bottleneck.
Listen to this summary
- agent
- agentic
- rag
- inference
- throughput
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