Your comprehensive guide to building high-performance, scalable machine learning systems
Build expertise in scalable, high-performance ML infrastructure covering compute, storage, networking, and cost optimization for both training and inference workloads.
Author: Jay Palat, CMU SEI (2022)
Comprehensive introduction to hardware factors, GPU vs CPU fundamentals, and ML pipeline stages.
Read Guide →Source: Meta Engineering Blog (2024)
Deep dive into Meta's 24,000-GPU clusters for Llama 3, covering hardware, networking, and storage at scale.
Read Case Study →Source: FinOps Foundation (2023)
Cost management strategies, common cost culprits, and real examples of GPU resource optimization.
Read Whitepaper →Author: Bijit Ghosh (2024)
Practical formulas for estimating GPU memory requirements and capacity planning for large models.
Read Guide →Source: Lambda Labs (2020)
Technical guide on designing on-premise GPU clusters, covering hardware, storage, and networking architecture.
Download PDF →Source: MLCommons
Industry-standard benchmarks for ML training and inference performance across different hardware.
Explore Benchmarks →Modern ML training is dominated by GPUs due to their massively parallel architecture. While CPUs have powerful cores optimized for sequential processing, GPUs contain thousands of smaller cores perfect for parallel operations.
| Aspect | CPU | GPU |
|---|---|---|
| Cores | 4-64 powerful cores | 1000s of smaller cores |
| Memory Bandwidth | ~90 GB/s | ~2,000 GB/s (A100) |
| Best For | Single-thread performance | Parallel matrix operations |
| ML Use Case | Data preprocessing, inference | Training, large model inference |
Memory Rule: Training typically requires 2-3× the model parameter size in GPU memory for gradients and optimizer state.
Example: A 1B parameter model (~4GB in FP32) needs ~8-12GB GPU memory for training.
Cost Example: GPT-3 training cost ~$4.6M, GPT-4 estimated >$100M in compute costs.
Memory Formula: Memory ≈ (Parameters × bytes/parameter / compression) × overhead
Example: 70B LLaMA model at FP16 needs ~140GB just for weights, often requiring multiple GPUs.
Optimization: Use quantization (INT8) and batching to maximize throughput.
Small clusters work well out-of-the-box
Large clusters need optimization
After topology-aware scheduling
Advanced clusters use fat-tree or dragonfly topologies for full bisection bandwidth. NVIDIA's DGX SuperPOD connects 140 nodes (1,120 GPUs) with minimal bottlenecks.
Built two identical 24k GPU clusters - one with 400 Gbps InfiniBand, one with 400 Gbps RoCE Ethernet. Both achieved similar performance with proper tuning, demonstrating architecture flexibility.
| Metric | Training | Inference | Tools |
|---|---|---|---|
| Throughput | Examples/sec, tokens/sec | Requests/sec, tokens/sec | MLPerf, custom benchmarks |
| Latency | Time per step/epoch | Time per request | Profilers, monitoring |
| Utilization | GPU %, CPU %, Memory % | GPU %, Network % | nvidia-smi, htop, iostat |
| Scalability | Linear scaling with GPUs | QPS scaling with replicas | Scaling experiments |
MLPerf provides standardized benchmarks for comparing systems. Key examples:
A startup wasted thousands monthly with 8 high-end GPUs sitting idle 40% of the time. Every unused GPU-hour is money lost!
| Factor | Cloud | On-Premise | Hybrid |
|---|---|---|---|
| Initial Cost | Low (pay-as-go) | High (hardware investment) | Medium |
| Scalability | Instant | Limited by hardware | Burst to cloud |
| Long-term Cost | Can be expensive at scale | Lower if well-utilized | Optimized |
| Control | Limited | Full control | Flexible |
"Balance innovation with budget: Achieving 99% accuracy is great — unless it doubles your infrastructure costs for a marginal gain."
Achieved: ~70% GPU utilization
Bottleneck: Occasional data loading delays
Solution: Increased data loader workers + tokenization caching
Deployment: 2-4 GPUs for inference with batching and quantization
Edge requires trade-offs: smaller models mean lower accuracy, but enable real-time response and reduce bandwidth costs. Consider model distillation to improve small model performance.