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How to efficiently store gradient data in large model training?

Created on 2025-08-14 18:16:51
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Efficiently storing gradient data in large model training is crucial for optimizing computational resources, reducing storage costs, and maintaining training performance. Here’s a breakdown of strategies, explanations, and examples, along with relevant cloud service recommendations.

1. Gradient Checkpointing (Activation Recomputation)

Instead of storing all intermediate activations during forward passes (which consume significant memory), gradient checkpointing trades computation for memory. During backpropagation, it recomputes certain activations instead of storing them. This reduces memory usage at the cost of extra FLOPs.

Example:
In a transformer model, you can checkpoint every few layers, recomputing hidden states during backward passes. This reduces GPU memory by up to 50%, allowing larger batch sizes or deeper models.

Cloud Relevance:
When using Tencent Cloud’s GPU instances (e.g., GN-series), gradient checkpointing helps maximize GPU utilization while minimizing memory overhead.

2. Mixed Precision Training (FP16/FP32)

Storing gradients in lower precision (FP16) reduces memory usage by half compared to FP32. Techniques like automatic mixed precision (AMP) maintain numerical stability while optimizing storage.

Example:
A large language model (LLM) training with FP16 gradients reduces memory consumption from 32GB (FP32) to 16GB (FP16), enabling larger batch sizes on the same hardware.

Cloud Relevance:
Tencent Cloud’s GPU instances with Tensor Cores (e.g., NVIDIA A100) accelerate mixed-precision training, improving throughput while reducing gradient storage needs.

3. Distributed Training with Gradient Aggregation

In data-parallel training, each GPU computes gradients locally, then synchronizes them via AllReduce (e.g., using NCCL or Horovod). Instead of storing all gradients separately, gradients are aggregated before updating model weights.

Example:
In a 16-GPU setup, each GPU computes local gradients, but only the averaged gradients are stored temporarily during synchronization, reducing peak storage requirements.

Cloud Relevance:
Tencent Cloud’s TKE (Tencent Kubernetes Engine) or BatchCompute can manage distributed training clusters, optimizing gradient aggregation across nodes.

4. Gradient Compression (Sparsification & Quantization)

  • Sparsification: Only store top-k gradients (e.g., 1% of largest values) and zero out the rest.
  • Quantization: Store gradients in lower-bit formats (e.g., 8-bit integers).

Example:
Applying 1-bit SGD (extreme quantization) reduces gradient storage by 32x, though it may require fine-tuning for convergence.

Cloud Relevance:
Tencent Cloud’s high-performance networking (VPC + RDMA support) minimizes latency when transmitting compressed gradients across nodes.

5. On-Demand Gradient Storage (Ephemeral Storage)

For short-lived gradient data (e.g., during a single backward pass), use fast ephemeral storage (NVMe SSDs) instead of persistent storage.

Example:
A training job writes gradients to local SSDs during computation, then discards them after the optimizer step, avoiding unnecessary cloud storage costs.

Cloud Relevance:
Tencent Cloud’s GPU instances with high-speed local NVMe storage provide low-latency gradient caching for high-throughput training.

  • GPU Compute: GN-series (NVIDIA A100/V100) for high-memory, mixed-precision training.
  • Storage: CBS (Cloud Block Storage) + CFS (Cloud File Storage) for scalable gradient persistence.
  • Distributed Training: TKE (Kubernetes) + VPC Networking for efficient gradient synchronization.
  • Cost Optimization: Spot Instances + Ephemeral Storage for transient gradient data.

By combining these techniques, large model training can achieve faster convergence, lower memory usage, and reduced storage costs while maintaining efficiency.