AI2026-05-17

Inference

Also known as: Inference / 推論 / モデル推論

The process of running a trained AI model on new inputs to produce predictions or generated outputs. In LLMs, this is the text-generation step — distinct from the training process.

Overview

Inference is the process of running a trained model on new inputs to produce outputs. For LLMs, this means token-by-token generation in response to a user prompt. Model parameters are not updated during inference. Inference cost, latency, and throughput determine the practical viability of an LLM-based product.

Inference optimization

Key optimizations include KV Cache, Speculative Decoding, quantization, FlashAttention, and request batching. Cloud APIs apply these transparently. For local inference, engines such as llama.cpp, vLLM, and TGI handle optimization.

Comprehensive guide to AI API cost optimization in the pay-per-use era. Covers Claude, GPT, Gemini pricing comparisons, 5 reduction techniques including prompt caching, batch APIs, local LLM hybrid operations, monthly cost simulations, and ROI calculation methods.

Gemma 4 System Requirements — 5–62GB VRAM, RTX 3060 to H100 by Variant (E2B/E4B/26B/31B) [2026 Guide]

Gemma 4 needs 5GB VRAM (E2B/E4B), 16GB (26B MoE), or 24-62GB (31B Dense) depending on quantization. Requirements by model: RTX 3060 to H100, Apple Silicon M1-M4, CPU-only operation, RAM sizing, and budget builds. Updated July 2026.

NVIDIA DGX Spark in 2026 — A Two-Stage Workflow for Code Migrations Where "Confidential Analysis Stays Local, Cloud LLMs Only Touch Sanitized Code"

An overview of NVIDIA DGX Spark (GB10 Grace Blackwell Superchip, 128GB unified memory, up to 1 PFLOP at FP4, $4,699) and a concrete two-stage workflow for confidential code-migration projects: analyze and sanitize locally, then hand a clean, PII-free representation to cloud frontier LLMs for the actual migration. Practical answers to the "executives won't approve cloud AI even with opt-out" problem.

Hybrid AI Strategy Guide — Achieving 50% Cost Reduction with Cloud API + Local LLM [2026]

A practical guide to reducing AI operational costs by over 50% with a hybrid AI strategy combining cloud APIs and local LLMs. Learn optimal architecture design and implementation steps using local models like Qwen 3.5 and DeepSeek R1 with Claude, GPT, and Gemini.

Mistral Small 4 Complete Guide — Unified Reasoning, Multimodal & Code in 119B MoE [2026]

Mistral Small 4, released March 2026, unifies reasoning, multimodal vision, and agentic coding in a 119B MoE model under Apache 2.0. Supports 11 languages including Japanese. Full specs, setup guide, and model comparisons.

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