In the news
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
NVIDIA Developer · Published · 3 min read
In 30 seconds
- What happened
- NVIDIA describes five co-design techniques to maximize HBM utility in AI inference, achieving 30x throughput per megawatt on Vera Rubin versus GB300.
- Why it matters
- Matters for engineers optimizing large language model serving at scale, especially those running agentic workloads with tool calls and long context windows.
- Watch out
- The 30x gain assumes specific agentic workload patterns with tool gaps and context reuse; results may differ significantly for other inference scenarios or batch profiles.
- kv cache
- token
The patterns behind this
- Agentic Context Engineering (Evolving Playbook)
- Context Editing & Tool-Result Clearing
- KV Cache Optimization
Each one covers how the technique works, when it earns its cost, and where it breaks.
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