No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

TRL now supports co-located vLLM, allowing training and inference to share the same GPUs, reducing idle time and improving throughput without extra hardware.

From the source
This approach is what we refer to as colocation. Training and inference are co-located on the same GPUs and coordinated via the same process group, allowing them to take turns smoothly — no extra hardware needed.
huggingface.co

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Announced: 03 Jun 2025 00:00 UTC
Detected: 03 Aug 2026 21:12 UTC (+426d after)

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