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GPU Optimization and ML Training Insights

Insights on GPU optimization, ML infrastructure, and reducing training costs.

DISTRIBUTED TRAINING

FSDP vs DDP vs DeepSpeed: A Practical Guide With Real Benchmarks

Real benchmarks comparing PyTorch FSDP, DDP, and DeepSpeed ZeRO for distributed ML training. Decision matrix by model size, memory formulas, and practical gotchas.

By allocguy · February 2026 · 12 min read

COST OPTIMIZATION

The Hidden Cost of LLM Fine-Tuning: Why Your First 10 Runs Are the Most Expensive

LLM fine-tuning costs are dominated by failed experiments, OOM retries, and hyperparameter guesswork. Real data on why your first 10 runs burn the most money, and how to fix it.

By allocguy · February 2026 · 10 min read

DEEP DIVE

Flash Attention Is All You Need: From Custom CUDA Kernel to PyTorch Native

How Flash Attention went from a Stanford research paper to the default attention backend in PyTorch. Real benchmarks: 10-20x memory savings, 3x training speedup, 75% GPU utilization on H100.

By allocguy · February 2026 · 12 min read

DATA ANALYSIS

Your GPU Is Idle 70% of the Time: Where ML Training Throughput Actually Goes

Real data from Microsoft, Meta, and Google shows most GPUs sit idle during ML training. 46% of issues are data pipeline bottlenecks, MFU rarely exceeds 50%, and 85% of fixes are simple code changes.

By allocguy · February 2026 · 10 min read

COST OPTIMIZATION

We Wasted $12k on GPUs: Here's What We Learned

How one ML team burned through $12,000 in GPU costs from over-provisioned instances, OOM failures, and idle hardware. Lessons on reducing ML training costs.

By allocguy · February 2026 · 8 min read

GPU GUIDE

A100 vs H100 vs L40S: Which GPU for Your Training Job?

Practical comparison of A100, H100, and L40S GPUs for ML training. Specs, pricing, VRAM, and which workloads each GPU is best suited for.

By allocguy · February 2026 · 10 min read