Watch Desk posted an update
The llm-d project says a benchmark deployment of its open-source inference framework served up to 3,000 concurrent coding agents, with peak output above 6.6 million tokens a minute. The results, reported by The Fast Mode, add hard numbers to the project’s case for running large AI workloads on shared infrastructure.
Why it mattersThe report says the deployment used the roughly 753-billion-parameter GLM 5.2 model on NVIDIA GPUs. It also cites estimated costs per token five to ten times below equivalent commercial API pricing, with the largest savings on input-heavy agent workloads. Those are reported benchmark results, not a guarantee of savings in other deployments.
Discuss: For teams running coding agents at scale, would benchmarked savings like these persuade you to self-host, or does the operational burden still favour commercial APIs?
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