JAX Memory Management Q&A
Explains why JAX reserves GPU memory, how to diagnose host vs device OOM, and fixes: batch size, mixed precision, remat, sharding.
NanoGPT Blog
Updates, guides, and insights
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229 posts found for 'models'
Smarter Load Balancing for AI Pipelines
Protect uptime first: use small models, caching, batch/live separation, tool offload, metrics-based routing and failover.
Domain Adaptation in Unsupervised Transfer Learning
Adapting labeled models to unlabeled target data fixes domain shift using alignment, adversarial training, and pseudo-labels.
ONNX Compression Tools: A Complete Roundup
Compress ONNX models to cut size and latency with quantization, pruning, and mixed-precision—practical tools and deployment tips.
AI Token Usage Reports: What to Include
Build clear AI token usage reports with token volume, cost per 1K, model/feature breakdowns, cache hit rates, and budgeting.
Offline AI Inference: Complete Guide 2026
Run AI models locally for privacy, lower latency, and cloud-free performance — hardware, quantization, GGUF formats, and tools.
Regulatory Compliance for AI Data Retention
Compare local, cloud, and enterprise AI retention options, risks, and best practices for regulatory compliance.
Top 5 Containerization Tools for AI Models
Compare the top five containerization tools for GPU-accelerated AI, covering GPU support, scalability, integrations, and security.
ARM vs x86 for AI: Key Differences
Compare ARM and x86 for AI workloads — ARM for energy-efficient edge inference; x86 for high-performance training and GPU-heavy tasks.
Best Practices for Azure OpenAI API Error Handling
Retry transient errors with backoff, monitor tokens/latency, and secure access to reduce Azure OpenAI API failures and costs.