PyTorch 2.x Compile in Production: When It Helps and When It Hurts
torch.compile delivers big speedups when it works and weird breakage when it does not. The 2026 production guide for when to enable it.
7 min read
Agentic AI & LLMs
Agentic AI & LLMs archive page 249 of 631
Agentic AI & LLMs
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
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PyTorch Lightning vs Raw PyTorch in 2026 Production
Lightning vs raw PyTorch for production AI in 2026 — productivity, performance, and the trade-offs that matter at scale.
7 min read
PyTorch Memory Optimization: Activation Checkpointing in Practice
Activation checkpointing trades compute for memory. The 2026 PyTorch patterns and where the tradeoffs actually pay off.
7 min read
PyTorch Profiler in Production: Finding the Real Bottleneck
The PyTorch Profiler reveals what is really slow in your training or inference. The 2026 patterns for diagnosing bottlenecks.
7 min read
Quantization-Aware Training in PyTorch: FP4, INT8, and BF16 Mixed
QAT is how you get small models without quality regressions. The 2026 PyTorch patterns for FP4, INT8, and BF16 mixed-precision training.
7 min read
Quantizing Embeddings: int8, Binary, and Matryoshka
Embedding quantization cuts storage 4-32x at modest recall cost. The 2026 quantization techniques and where each one wins.
7 min read
Qwen3 Deep Dive: Agentic Tool Use and Multilingual Performance
Qwen3 is the strongest open-weights agentic model in 2026 by several measures. A deep dive on its tool use, multilingual capability, and architecture.
8 min read
RAG Caching Layers: Hit Rates and Cost Reduction Strategies
Cache the right RAG layer and you cut cost 60-80 percent. The 2026 multi-layer cache designs and what to cache where.
8 min read
RAG Evaluation Frameworks 2026: RAGAS, TruLens, and DeepEval in Practice
Three RAG evaluation frameworks compared on real production RAG pipelines: RAGAS, TruLens, and DeepEval. Strengths, weaknesses, when to use each.
8 min read