AI - IoT Digital Twin PLM

Spot GPU Training: Checkpointing, Preemption Handling and Fault-Tolerant LLM Jobs (2026)

By MPRAUTO MPRAUTO October 14, 2026AINo Comments

Spot GPU Training: Checkpointing, Preemption Handling and Fault-Tolerant LLM Jobs (2026) A reserved eight-GPU node costs several times what the same node costs on the spot market, and for a…

RAG Latency Budget: Optimizing Retrieval, Reranking and Streaming for Production (2026)

By MPRAUTO MPRAUTO October 14, 2026AINo Comments

RAG Latency: Optimizing Retrieval, Reranking and Streaming for Production (2026) Most retrieval-augmented generation demos feel fast because they run against ten documents, one user and a warm laptop. Production feels…

Neural Operators (FNO, DeepONet) vs PINNs for Engineering Surrogate Models (2026)

By MPRAUTO MPRAUTO October 14, 2026AINo Comments

Neural Operators vs PINN: FNO and DeepONet as Engineering Surrogate Models (2026) Most engineers meet physics-informed neural networks first, because the pitch is irresistible: write the governing equation into the…

Anthropic Claude Opus 4.6: Architecture & Capabilities (2026)

By MPRAUTO MPRAUTO October 14, 2026AINo Comments

Anthropic Claude Opus 4.6: Architecture & Capabilities (2026) - updated October 2026 with verified facts, diagrams and FAQ.

Ring Attention and Context Parallelism: How Long-Context LLMs Are Trained Across GPUs (2026)

By MPRAUTO MPRAUTO October 12, 2026AI2 Comments

Ring Attention and Context Parallelism: How Long-Context LLMs Are Trained Across GPUs A single GPU cannot hold the activations of a one-million-token sequence, no matter how clever your kernel is.…

Document AI in 2026: OCR Pipelines vs Vision-Language Models (Docling, olmOCR, Mistral OCR)

By MPRAUTO MPRAUTO October 12, 2026AINo Comments

Document AI OCR in 2026: Pipelines vs Vision-Language Models Most retrieval-augmented generation (RAG) systems fail long before the language model gets involved. They fail at the PDF. A two-column paper…

Model Merging for LLMs: mergekit, TIES, DARE, SLERP and Evolutionary Merging (2026)

By MPRAUTO MPRAUTO October 12, 2026AINo Comments

Model Merging for LLMs: mergekit, TIES, DARE, SLERP and Evolutionary Merging Most teams that need a model with two skills assume they must train for both: more data, more GPU…

pgvectorscale vs pgvector: StreamingDiskANN vs HNSW in Postgres (2026)

By MPRAUTO MPRAUTO October 11, 2026AI1 Comment

pgvectorscale vs pgvector compared: StreamingDiskANN vs HNSW, SBQ quantization, label filtering, build time, memory, recall and when each wins.

NVIDIA Dynamo and LMCache: Disaggregated LLM Serving and KV Cache Offload (2026)

By MPRAUTO MPRAUTO October 11, 2026AI5 Comments

NVIDIA Dynamo and LMCache explained: prefill/decode disaggregation, KV-aware routing, NIXL transfers, tiered KV offload, and when the complexity pays off.

TinyML on Microcontrollers: TensorFlow Lite Micro, INT8 Quantization and CMSIS-NN Inference

By MPRAUTO MPRAUTO October 10, 2026AI2 Comments

TinyML explained: running neural networks on microcontrollers with TensorFlow Lite Micro, INT8 quantization, CMSIS-NN kernels, memory budgeting and.

Spot GPU Training: Checkpointing, Preemption Handling and Fault-Tolerant LLM Jobs (2026)

RAG Latency Budget: Optimizing Retrieval, Reranking and Streaming for Production (2026)

Neural Operators (FNO, DeepONet) vs PINNs for Engineering Surrogate Models (2026)

Anthropic Claude Opus 4.6: Architecture & Capabilities (2026)

Ring Attention and Context Parallelism: How Long-Context LLMs Are Trained Across GPUs (2026)

Document AI in 2026: OCR Pipelines vs Vision-Language Models (Docling, olmOCR, Mistral OCR)

Model Merging for LLMs: mergekit, TIES, DARE, SLERP and Evolutionary Merging (2026)

pgvectorscale vs pgvector: StreamingDiskANN vs HNSW in Postgres (2026)

NVIDIA Dynamo and LMCache: Disaggregated LLM Serving and KV Cache Offload (2026)

TinyML on Microcontrollers: TensorFlow Lite Micro, INT8 Quantization and CMSIS-NN Inference

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