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slime-rl-training

Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.

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# slime: LLM Post-Training Framework for RL Scaling

slime is an LLM post-training framework from Tsinghua's THUDM team, powering GLM-4.5, GLM-4.6, and GLM-4.7. It connects Megatron-LM for training with SGLang for high-throughput rollout generation.

## When to Use slime

### Hardware Requirements

- **Minimum**: 2x A100 80GB (160 GB total VRAM) for 7B models
- **Recommended**: 4x H100 80GB (320 GB total VRAM) for 70B+ models
- **Multi-node**: InfiniBand required for cross-node communication
- **Storage**: NVMe with 5+ TB for checkpoints

> **Cost Warning**: slime workloads require significant GPU resources. Budget $10-25/hr