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Explora todas las tecnologías del grafo por tipo y categoría.
Por tipo
Por categoría
- Algoritmos 30
- Modelos 16
- Modelos de embedding 8
- Técnicas 35
- Prompting 10
- Frameworks 12
- Frameworks de LLM 6
- Bases de datos 3
- Bases de datos vectoriales 8
- Motores de búsqueda 4
- Infraestructura 15
- MLOps 4
- Evaluación 6
- Hardware 11
- Seguridad y alineación 6
- Aplicaciones 7
- Multimodal 5
- Agentes de IA 4
- Benchmarks 11
- Conjuntos de datos 7
15 technologies shown
- vLLM tool High-throughput inference server built around paged attention and continuous batching.
- Ollama tool Runs open-weight models locally behind a simple command and HTTP API.
- Inference concept Running a trained model to produce output, as opposed to training it.
- Prompt Caching technique Reusing the computed state of an unchanged prompt prefix.
- Tokenizer tool Component that converts text to tokens and back.
- Ray framework Distributed execution framework for scaling Python workloads.
- Safetensors infrastructure File format for model weights designed to avoid arbitrary code execution.
- llama.cpp tool CPU-first inference for quantised models, in portable C++.
- ONNX infrastructure Interchange format for models, so a trained model runs on another runtime.
- GGUF infrastructure Quantised model format designed for fast loading and CPU inference.
- SGLang tool Serving engine built around reusing shared prompt prefixes.
- Triton Inference Server tool NVIDIA's server for deploying models from several frameworks behind one API.
- TensorRT-LLM tool NVIDIA's compiler and runtime for optimised LLM inference on its GPUs.
- Text Generation Inference tool Hugging Face's serving stack for transformer models.
- KV Cache technique Cached attention keys and values that make generating one token at a time affordable.