AI - IoT Digital Twin PLM

LLM Semantic Caching Architecture: Cut Inference Cost and Latency (2026)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

A semantic caching architecture for LLM apps: exact vs embedding-similarity cache tiers, thresholds, invalidation, eviction, and the cost/latency math behind GPTCache-class systems.

Small Language Models on Device: Edge Inference Architecture (2026)

By MPRAUTO MPRAUTO July 10, 2026AI1 Comment

How to run small language models (SLMs) on-device: model sizing, distillation, quantization, NPU acceleration, memory budgets, and when a 1-8B SLM beats a cloud LLM.

Google Gemini 3.5 Pro Explained: Architecture, Benchmarks, and 2M Context (2026)

By MPRAUTO MPRAUTO July 10, 2026AINo Comments

Google Gemini 3.5 Pro explained: the 2M-token context flagship, architecture, training, benchmark scores, pricing, and how it compares to GPT-5.6 and Claude.

Google Gemini 3.5 Flash Explained: Architecture, Benchmarks, and Deployment (2026)

By MPRAUTO MPRAUTO July 8, 2026AI1 Comment

Google Gemini 3.5 Flash explained: the MoE multimodal architecture, context window, real 2026 benchmarks, pricing, latency, and how it compares to GPT and Claude.

Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench

By MPRAUTO MPRAUTO July 8, 2026AI2 Comments

A deep dive into 2026 AI agent benchmarks: SWE-bench Verified, GAIA, and tau-bench — what they measure, how they leak, and how to read agent leaderboards honestly.

Constrained Decoding: Architecture for Guaranteed-Valid LLM Output (2026)

By MPRAUTO MPRAUTO July 8, 2026AINo Comments

How constrained decoding guarantees valid LLM output: grammars, FSAs, token masking, JSON-schema enforcement, and where structured generation breaks in production.

DeepSeek V4 Explained: Architecture, Sparse Attention, Benchmarks, and Deployment (2026)

By MPRAUTO MPRAUTO July 2, 2026AINo Comments

DeepSeek V4 explained: the 1.6T-parameter MoE architecture, Compressed Sparse Attention, 1M-token context, SWE-bench and reasoning benchmarks, pricing, and how to deploy it.

DeepSeek V4 Explained: Architecture, Sparse Attention, Benchmarks, and Deployment (2026)

By MPRAUTO MPRAUTO July 2, 2026AI2 Comments

DeepSeek V4 explained: the 1.6T-parameter MoE architecture, Compressed Sparse Attention, 1M-token context, SWE-bench and reasoning benchmarks, pricing, and how to deploy it.

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

By MPRAUTO MPRAUTO July 2, 2026AINo Comments

Long-context LLM benchmarks in 2026: why 1M-token windows do not mean 1M-token reasoning, RULER, NIAH, effective context length, and how to test long-context models properly.

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

By MPRAUTO MPRAUTO July 2, 2026AINo Comments

Long-context LLM benchmarks in 2026: why 1M-token windows do not mean 1M-token reasoning, RULER, NIAH, effective context length, and how to test long-context models properly.

LLM Semantic Caching Architecture: Cut Inference Cost and Latency (2026)

Small Language Models on Device: Edge Inference Architecture (2026)

Google Gemini 3.5 Pro Explained: Architecture, Benchmarks, and 2M Context (2026)

Google Gemini 3.5 Flash Explained: Architecture, Benchmarks, and Deployment (2026)

Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench

Constrained Decoding: Architecture for Guaranteed-Valid LLM Output (2026)

DeepSeek V4 Explained: Architecture, Sparse Attention, Benchmarks, and Deployment (2026)

DeepSeek V4 Explained: Architecture, Sparse Attention, Benchmarks, and Deployment (2026)

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

Long-Context LLM Benchmarks 2026: RULER, Effective Context, and the Lost-in-the-Middle Problem

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