AI Frameworks

Chain-based LLM framework — prompts, chains, agents, memory

Data framework for LLMs — RAG, agents, data connectors

High-throughput LLM serving — PagedAttention, continuous batching

HuggingFace production serving — optimized for inference

Efficient LLM serving — compression, quantization, high throughput

HuggingFace unified model framework — NLP, vision, audio, multimodal

State-of-the-art diffusion models for image, video, and audio generation

Programming—not prompting—language models. Declarative specs compiled to optimized prompts.

Fine-tune LLMs 2-5x faster with 70% less VRAM — consumer GPU friendly

YAML-configurable fine-tuning for LoRA, QLoRA, and full fine-tuning

High-performance LLM serving with structured output — JSON mode, regex constraints

Unified ML model serving — any model type, production-ready

Distributed vector database — cloud-native, billion-scale

High-performance vector database — billion-scale similarity search

Multimodal vector database — GraphQL, REST, hybrid search

Embedded vector database — simplest path to production RAG

Open-source RAG engine — deep document understanding

Document conversion — PDF, Word, Excel, PowerPoint to markdown

PDF to markdown — fast, accurate, open-source

Document preprocessing — any format to LLM-ready chunks

NVIDIA optimized LLM inference — maximum speed on NVIDIA GPUs

Embedded vector database — simplest path to production RAG

High-performance vector database — billion-scale similarity search

Multimodal vector database — GraphQL, REST, hybrid search