In practice
It is the engine behind ChatGPT, Claude, Gemini. When you embed an LLM into your product you pay per token and get a service that reads and writes text. Quality depends heavily on the chosen model and the prompt you give it.
Related terms
Seen in the wild
62 entries mentioning it- MediumMistral releases Devstral Small: 7B coding model for agentic tasks on consumer GPU
- HighFigure AI releases Figure 02 autonomy update: fully autonomous warehouse picking without human teleoperation
- MediumOllama 0.9: concurrent model serving, multi-GPU split, and REST API v2 for local AI
- MediumLocal AI 2025: Ollama, MLX LM, Apple Foundation Models triple the speed
- MediumPrivate LLM: models up to 7B directly on iPhone and Mac, fully offline
- MediumvLLM v0.7: chunked prefill by default and a redesigned V1 engine
- HighNVIDIA NIM 1.0: Containerized LLM Inference with OpenAI-Compatible API
- MediumWebLLM and LLM in WASM: browser-based LLM inference via WebGPU, no server needed
- MediumContinuous Batching for LLM Serving: survey and state of the art of Orca, vLLM, SGLang, TGI
- HighDeepMind: 60+ cases of Specification Gaming in LLMs documented
- MediumFlashInfer 0.2: attention library for LLM serving with paged KV cache and RoPE fusion
- HighPrefill/decode disaggregation: separate GPUs for low TTFT and high throughput
- HighKV Cache Quantization FP8/INT8: Double User Density per GPU
- HighAnythingLLM 1.0: the complete local RAG stack for enterprise use
- MediumLLM Compressor: unified toolkit for quantization and sparsity with native vLLM integration
- MediumCyberSecEval 2: Meta's LLM cybersecurity benchmark
- MediumDify 0.7: visual agentic workflows with integrated RAG and 10+ LLMs
- MediumDrEureka: LLM automates simulation-to-real transfer without manual tuning
- MediumNeMo Guardrails 0.8: NVIDIA's framework for adding safety rails to any LLM
- MediumMicrosoft RoboGen: generating robot tasks, skills and environments from text