Speeding Up Distributed Training with vLLM, Flash Attention, and Checkpoint Resuming

Issue

While scaling up model fine-tuning with distributed training, we encountered several performance bottlenecks and reliability issues:

• Slow inference and response latency with traditional model serving
• Memory overloads with large models in multi-GPU settings
• Unreliable checkpoint resumption during multi-node training runs
• Suboptimal memory usage and throughput with standard attention implementations

Solution

We applied the following stack of improvements to significantly boost speed and reduce memory usage, especially in distributed settings:

1. Use vLLM with Updated torchrun + transformers 0.16+

• Switched to vLLM for faster inference with paged attention.
• Ensured compatibility with Transformers v0.16 and torchrun for clean distributed job launches.
• Set up table-stable model loading to support smooth rollout.

2. Enable Flash Attention (v2)

• Replaced standard attention with Flash Attention v2, yielding up to 3× speedup and major VRAM savings during training.
• Confirmed compatibility with our model (no rotary embedding or special kernels blocking Flash).

3. Gradient Checkpointing

• Enabled gradient checkpointing to reduce memory usage at the cost of minimal compute overhead.
• Applied it selectively, avoiding attention blocks where Flash is already efficient.
• Greatly improved trainable model size and batch size scalability.

4. Use LoRA for Efficient Fine-Tuning

• Added LoRA adapters for parameter-efficient tuning of large language models.
• Enabled LoRA weight merging post-training.
• Reduced training time and memory usage while retaining model performance.

5. Fix Checkpoint Resume Bug

• Patched training logic to ensure checkpoint recovery works after mid-run failures in distributed settings (especially with torchrun + deepspeed).
• Ensured optimizer and scheduler states resume correctly to avoid performance drift.

🚀 Outcome

With this setup:

• Training was significantly faster and more stable
• We ran 7B+ parameter models across multiple nodes without hitting OOM
• Checkpoint resume was robust, making experimentation safer and faster
• Overall system throughput increased by 2–4× depending on hardware and model config

💡 Takeaway

Modern LLM fine-tuning pipelines benefit immensely from combining:

• vLLM + Flash Attention for memory-efficient speed
• Gradient checkpointing + LoRA for scalable adaptation
• Robust checkpointing logic to avoid reruns

This stack is essential for practical, scalable LLM training on clusters.