Ultimate Guide to AI Model Storage Needs
Practical guide to AI storage: VRAM/RAM sizing, NVMe vs HDD, checkpoints, object storage, and caching strategies to prevent GPU stalls and cut costs.
NanoGPT Blog
Updates, guides, and insights from the NanoGPT team
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AI Infrastructure: Redundancy vs. High Availability
Compare redundancy and high availability for AI infrastructure — tradeoffs in cost, recovery time, and how combining them improves resilience.
Latency Profiling for Large Language Models
Guide to profiling LLM latency: measure TTFT, TPOT, and ITL; use PyTorch, Nsight, and tracing; optimize batching, quantization, and memory bandwidth.
Dynamic Load Balancing: Role of Round Robin Algorithms
Compare Round Robin, Weighted and Dynamic methods, their trade-offs, and the best use cases for web, cloud, and AI workload balancing.
How to Monitor AI Models for Errors
Monitor AI models to catch silent failures—track hallucinations, data drift, latency, token costs, set alerts, and automate retraining.
RNN Variants: Performance Comparison
Compare Vanilla RNNs, LSTMs, and GRUs—memory, speed, parameter trade-offs and best use cases for short, medium, and long sequence tasks.
5 Partitioning Methods for Multi-GPU Training
Compare five multi-GPU partitioning strategies—data, model, pipeline, sharded, and fully sharded—to balance memory, communication, and scalability.
Scaling Depth vs. Width: Cost Trade-Offs
Wider models win for throughput; deeper models win for reasoning — the right mix, not raw size, controls AI cost, latency, and performance.
Multimodal Data Pipelines: Scalability Best Practices
Treat multimodal pipelines as first-class systems: modularize by modality, partition and shard data, autoscale components, and reduce wasted compute.
Top 5 Tools for Scalable Churn Prediction
Compare five scalable churn prediction tools — features, AI models, integrations, and pricing to match small teams through large enterprises.