cluster
Training Infrastructure
Training and fine-tuning infrastructure: OOM prevention, VRAM estimation, memory profiling, multi-GPU and multi-node scaling, large datasets.
6 articles
Subclusters
Articles
May 22, 2026
Multi-GPU Tensor Parallelism Setup: Configuration and Optimization Guide
A 70B model needs about 140GB in FP16 and does not fit on one 80GB GPU. Tensor parallelism splits weight matrices across devices, at the cost of four all-reduce collectives per transformer layer in a training step.
May 20, 2026
LoRA vs Full Fine-Tuning Memory Cost: VRAM Math
You have a 24GB GPU and an 8B model. The math says it should fit, but your training script crashes with an OOM error before the first epoch. We break down the exact VRAM requirements for full fine-tuning versus LoRA.
May 18, 2026
FP8 Training on H100: Benchmarks and Memory Savings
Training a 70-billion parameter model in BF16 requires hundreds of gigabytes of GPU memory. Shifting to FP8 precision on NVIDIA H100s halves the bytes per element for the tensors actually held in FP8, master weights and optimizer states stay in higher precision, and NVIDIA's NeMo measurements show 1.30x throughput on Llama 3 8B and 1.43x on Llama 3 70B versus BF16.
May 16, 2026
Multi GPU Distributed Training Setup Guide: Frameworks & Infrastructure
Scaling from a single GPU to a multi-node cluster introduces complex communication bottlenecks and fatal memory errors. Learn how to configure DDP, FSDP, and DeepSpeed while optimizing your infrastructure for maximum throughput.
February 23, 2026
Maximizing VRAM: Gradient Checkpointing Memory Savings Guide
Out-of-memory errors are the primary bottleneck for scaling deep learning models beyond a few billion parameters. Gradient checkpointing offers a strategic trade-off, allowing engineers to train massive architectures on existing hardware by recalculating activations on the fly.
February 23, 2026
ZeRO-3 vs FSDP: A Deep Dive into Memory Efficiency for LLMs
Scaling large language models requires moving beyond standard data parallelism to overcome the memory wall. This technical guide compares DeepSpeed ZeRO-3 and PyTorch FSDP to help engineers optimize GPU utilization and eliminate out-of-memory errors.