#VLLM VRAM Usage GRPO

13 messages · Page 1 of 1 (latest)

drifting totem
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I am following the GRPO notebook for Deepseek-R1-Qwen3-8B. When enabling vllm, the memory usage goes through the roof and I run out of memory on an H200, even when setting gpu_memory_utilization to be something low like 0.5.

This is with context length 8k and 2 generations during GRPO training.

Running without fast_inference seems to work fine and does not use much memory. I'm wondering if I am doing something incorrectly.

azure robinBOT
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When using vLLM (fast_inference=True) with Unsloth for GRPO, VRAM usage can spike due to the need to keep both inference (vLLM) and training engines in memory, especially with long context and multiple generations. Even with gpu_memory_utilization set low, vLLM may still allocate more VRAM than expected, and Unsloth's standby/sleep mode is designed to help, but issues with OOM (out of memory) can still occur, particularly on large models or when resuming from checkpoints. This is a known challenge, and sometimes lowering context length, batch size, or num_generations is necessary. Also, ensure you are using the latest Unsloth and vLLM versions, as recent updates have improved memory handling, and try enabling Unsloth Standby by setting os.environ["UNSLOTH_VLLM_STANDBY"] = "1" before importing Unsloth to further reduce VRAM usage during RL training.

If you still encounter OOM, consider running without vLLM (fast_inference=False) for more stable memory usage, or experiment with further reducing context length and batch size. Note that vLLM and Unsloth share memory space, but high VRAM usage is expected for long context and multiple generations, and some users have reported that even with low gpu_memory_utilization, vLLM may not respect the limit strictly in standby mode. For more details and troubleshooting, see the official Unsloth RL memory guide and related GitHub issues: Unsloth RL Memory Guide, Issue 3328, Issue 2632.

Would you like a step-by-step breakdown or code example for your setup?

Sources:

💡 **Hint:** Mention @RunLLM in the thread for followups.
drifting totem
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I am setting that environment variable.

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Ok, I think it is the environment variable causing the issue. Thanks

drifting totem
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@azure robin I am doing GRPO on gpt-oss-20b on a B200 with 180GB VRAM, sequence length of around 6k and it's using flex attention, and it's running out of memory. I am using only 4 generations, batch size of 1. What might be the issue?

azure robinBOT
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Even with flex attention, GRPO on gpt-oss-20b at 6k context and 4 generations can exceed 180GB VRAM due to the large memory required for MoE layers, activations, and the GRPO loss computation, especially with long context. Flex attention reduces VRAM but does not eliminate the high memory cost of MoE and multi-generation GRPO. This is a known limitation, and Unsloth documentation and user reports confirm that OOM can still occur at these settings, especially for 20B+ models with MoE (Unsloth Issue 3411, Unsloth GPT-OSS Guide).

To mitigate, try reducing context length, number of generations, or use QLoRA (4-bit) if not already. Also, ensure you are not running validation with eager attention, which can cause OOM even if flex is used for training (Unsloth Issue 3363). Would you like a detailed breakdown of memory usage or further optimization tips?

Sources:

💡 **Hint:** Mention @RunLLM in the thread for followups.
drifting totem
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@azure robin do I need to install xformers to get the memory efficient version of flex attention or to save VRAM in general?

azure robinBOT
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No, you do not need to install xformers to use Unsloth Flex Attention or to get the most memory-efficient VRAM usage—Flex Attention is implemented with custom Triton kernels and does not depend on xformers. Flex Attention is the recommended and most efficient option for gpt-oss and similar models (Unsloth GPT-OSS Guide).

xformers is optional in Unsloth and is only used for certain models or fallback attention implementations; Flex Attention is separate and more efficient for supported models (Unsloth Issue 3346). Would you like more detail on when xformers is needed?

Sources:

💡 **Hint:** Mention @RunLLM in the thread for followups.
drifting totem
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@azure robin How much is the context length I can use for GRPO in gpt-oss-20b for 180 GB of VRAM?

azure robinBOT
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For GRPO on gpt-oss-20b, Unsloth reports that with QLoRA (4-bit), you can fit the model and training in as little as 14GB VRAM, but for long context and multiple generations, memory usage increases rapidly. With 180GB VRAM, you should be able to use context lengths well above 6k—users have reported running 20k context with 8 generations on 80GB H100s, and Unsloth claims 5–10x longer context than standard methods (Unsloth GPT-OSS Guide, Unsloth Blog).

However, actual maximum context depends on batch size, number of generations, and other settings. For 180GB VRAM, you should be able to reach at least 20k–40k context with batch size 1 and 4 generations, but MoE memory spikes and GRPO loss computation may still cause OOM at higher settings. Always test incrementally upward from 10k to find your hardware's limit. Would you like a step-by-step guide to estimate your exact maximum?

Sources:

💡 **Hint:** Mention @RunLLM in the thread for followups.
drifting totem
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@azure robin why is it that on 8k context 4 generation batch size 1 I run out of memory?

azure robinBOT
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Even with 180GB VRAM, running GRPO on gpt-oss-20b with 8k context, 4 generations, and batch size 1 can cause OOM because GRPO requires storing activations and logits for each generation and the MoE layers in gpt-oss-20b are extremely memory-intensive. Flex Attention reduces VRAM, but the combination of long context, multiple generations, and MoE still results in very high memory usage—users have reported OOM at similar settings (Unsloth Issue 3411, Unsloth Blog).

The memory cost grows with context length × generations × model size, and MoE models like gpt-oss-20b are especially demanding. Reducing context length, number of generations, or switching to a smaller model are the main ways to avoid OOM. Would you like a breakdown of memory usage or optimization tips?

Sources:

💡 **Hint:** Mention @RunLLM in the thread for followups.
dense snow