[cleanup] Remove NCCL_CUMEM_ENABLE=0 from prepare_runtime_environment

[erictang000]

We previously had the following snippet in prepare_runtime_environment

# NOTE (charlie): See https://github.com/vllm-project/vllm/blob/c6b0a7d3ba03ca414be1174e9bd86a97191b7090/vllm/worker/worker_base.py#L445
# and https://docs.vllm.ai/en/v0.9.2/usage/troubleshooting.html?h=nccl_cumem_enable#known-issues
    if cfg.generator.inference_engine.weight_sync_backend == "nccl":
        env_vars["NCCL_CUMEM_ENABLE"] = "0"

The NCCL bug that required this was resolved in NCCL 2.22.3, and this override was removed from vLLM:

NCCL: NVIDIA/nccl#1234
vLLM: vllm-project/vllm#24141

Since the resolved NCCL version is shipped with PyTorch, and we are pinned to 2.10.0 (NCCL 2.26.2), it seems safe to remove this env var for older NCCL versions.

In fact, Nemo-RL actually sets this env-var to 1 (link).

Verified that GSM8K still works with this flag removed both colocated and non-colocated, and with vllm tp=2

We see that max gpu memory utilization is also slightly lower with this env var removed, as it enables newer NCCL version memory optimizations (as mentioned in vllm-project/vllm#24141):

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[devin-ai-integration]

✅ Devin Review: No Issues Found

Devin Review analyzed this PR and found no potential bugs to report.

View in Devin Review to see 1 additional finding.

[gemini-code-assist]

Code Review

This pull request removes the logic that sets the NCCL_CUMEM_ENABLE environment variable to '0' when the NCCL weight synchronization backend is used during runtime environment preparation. I have no feedback to provide as there are no review comments.