Tag: LLM Training

6 articles

• LLM Context Parallel in Production: Breaking Long-Context Bottlenecks with Sequence Sharding, Ring/Ulysses Communication, and Causal Load Balancing

  An in-depth guide to Context Parallel for long-context LLM training, covering Ring Attention vs. Ulysses communication, causal load balancing strategies, and production deployment checks to scale context from 8K to 128K+ tokens on multi-GPU clusters.

  07/16/2026 LLM Training / Context Parallel / Distributed Systems / Long Context / Ring Attention

• LLM Gradient Accumulation in Production: Token-Normalized Loss, DDP no_sync, and Scheduler Alignment to Avoid Equivalent Batch Distortion

  Gradient accumulation seems like simple batch summing, but variable-length samples, distributed sync, mixed precision, and learning rate schedulers silently alter the equivalent batch. This article dives into production-grade solutions like token normalization, DDP no_sync, AMP update boundaries, and tail window handling to achieve mathematically consistent gradient accumulation in LLM fine-tuning.

  07/14/2026 LLM Training / Gradient Accumulation / PyTorch DDP / Fine-Tuning / Mixed Precision

• LLM Activation Checkpointing in Production: Selective Recompute, Non-Reentrant Mode, and RNG Consistency to Reduce Training Memory

  During large model training, activations often exhaust GPU memory before parameters. This article explores selective recomputation, non-reentrant mode, RNG state consistency, and engineering gating strategies to reduce peak memory while preserving gradient correctness and controlling throughput loss.

  07/14/2026 LLM Training / Activation Checkpointing / PyTorch / GPU Memory / Distributed Training

• LLM FP8 Fine-Tuning in Production: Amax Scaling, Layer Whitelists, and BF16 Fallback for Stable Convergence

  FP8 reduces compute and memory for LLM fine-tuning, but poor scaling, outliers, and distributed sync issues can cause silent accuracy degradation. This guide provides a complete engineering solution with Amax monitoring, layer whitelists, BF16 fallback, dual-track evaluation, and release gates.

  07/14/2026 LLM Training / FP8 / Transformer Engine / Fine-tuning / MLOps / Mixed Precision Training / Distributed Training / Amax Scaling

• LLM SFT Sequence Packing in Production: Boosting Throughput with Sample Boundaries, Loss Mask, and Length Buckets

  A practical guide to implementing safe Sequence Packing for SFT training, covering attention isolation, loss masking, position IDs, length bucketing, and validation gates to eliminate padding waste without cross-sample contamination.

  07/14/2026 LLM Training / SFT / Sequence Packing / FlashAttention / Data Collator

• Distributed LLM Fine-Tuning Checkpoint Resume in Production: Sharded Checkpoints, Atomic Commits, and World Size Changes

  A practical guide to production-grade checkpoint and resume for distributed LLM fine-tuning. Covers sharded checkpoints, atomic commits, optimizer/RNG state recovery, world size changes, and fault injection drills to prevent silent training drift.

  07/13/2026 LLM Training / Distributed Checkpoint / FSDP / Fault Tolerance