Features
On-device LLM inference with X-Bit quantization (sub-4-bit)
No cloud dependency; all data stays on device
Real-time inference for voice and text applications
Integrates with Picovoice voice AI stack (wake word, STT, TTS)
Supports RAG (Retrieval-Augmented Generation) for document QA
Low latency and offline operation
Privacy-preserving: no data leaves the device
SDK for multiple platforms (Android, iOS, Linux, macOS, Windows, Web, Python)
Customizable model compression with picoCompression
Benchmarked against GPTQ, GGUF, and SpinQuant for accuracy/speed
Distributed model training across GPU clusters
Multimodal data curation (video, images, text, audio)
Batch embedding generation with Sentence Transformers
Post-training LLM inference with vLLM and SGLang
Elastic scaling with last-mile data preprocessing
Fine-grained hardware allocation (CPU, GPU, TPU, NVL72)
Multi-cloud orchestration across GPU providers
Ray-native distributed object store and RDMA transport
Automatic cluster provisioning and scaling
GPU observability and advanced monitoring
Agent-first experience with Python APIs
Serverless execution with Python decorators
Bring Your Own Cloud (BYOC) deployment
Integration with PyTorch, vLLM, SGLang, XGBoost
On-premises deployment support via BYOC
