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NanoGPT Blog
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407 posts found for 'models'
Energy Efficiency in AI Pipelines: Research Insights
Measure per-stage energy, cut token waste, and match hardware to workload to reduce AI inference cost and power.
Profiling PyTorch Models: Bottleneck Analysis
Identify where model time is spent—data, compute, memory, or communication—and fix it using torch.utils.bottleneck, torch.profiler, and targeted retests.
Rate Limiting in AI Gateways: Complete Guide
Use request, token, and spend limits to protect AI gateway uptime, control costs, and prevent noisy tenants from taking over.
Edge AI vs. Cloud AI: Cost and Latency Comparison
Edge offers sub-50 ms latency and lower bandwidth at higher upfront cost; cloud gives pay-as-you-go scaling for light, bursty workloads.
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.
Compiler Optimizations for AI Models
Compilers yield the biggest AI inference gains—fusion, layout tuning, SIMD, and BF16/INT8 with careful profiling.
Scaling AI Validation Across Data Centers
Unified blueprint to validate models, data, and infrastructure across regions with shared metrics, gates, chaos tests, and ownership.
Real-Time AI Apps with Azure OpenAI APIs
Real-time AI apps only succeed when streaming speed, tight prompts, regional deployment, and governance are built together.
Real-Time Upscaling: Algorithm Benchmarks
Compare upscaling models by speed vs. quality: latency, PSNR/SSIM/LPIPS, VRAM needs, and TensorRT speedups for 2x–4x.