Lifeboat is an LLM inference engine purpose-built for the concurrency and long-running context that agentic AI workloads demand. It runs on infrastructure enterprises already own, delivering 2-6x more concurrent capacity on the same GPU hardware while eliminating per-token cloud costs and keeping data inside the enterprise perimeter.
A single chatbot question is one model call. A single agent task can be dozens — planning, tool calls, MCP roundtrips, retries, and reflection, all riding on a conversation that keeps growing.
Lifeboat is purpose-built for the way agents actually consume inference. Instead of treating every request like an isolated chatbot prompt, it manages memory, scheduling, and model precision for fleets of long-running, tool-using agents on shared hardware. Four things make that possible:
The payoff: 2x or more concurrent agent sessions on the same GPU, full model quality intact, and performance that holds steady as load climbs.
Lifeboat isn't just a faster engine. It's a managed control plane for inference at scale, with a built-in cluster manager that orchestrates Lifeboat across nodes — and manages your other inference engines alongside it. One place to deploy, route, monitor, and govern the GPUs serving your agents.
Your data stays where it belongs — inside your perimeter, governed by your policies, and never exposed to shared AI services.
Use, tune, and control the models that power your workflows. Own what you build instead of depending on opaque external systems.
Run inference on infrastructure you control, from private cloud to on-prem systems and edge deployments.
Benchmarks used a Qwen 30B A3B model and long-context workloads that simulate real enterprise agents processing large documents in parallel.
Performance is only part of the challenge. Production AI requires governance, orchestration, security, and operational controls that raw inference engines don't provide. Lifeboat combines high-performance inference with the infrastructure required to run AI at scale.
Lifeboat is an enterprise-grade, self-hosted LLM inference platform that combines high-performance model serving with a complete operational control plane. It delivers roughly 2× concurrent user capacity per GPU without quantizing model weights, while providing a web dashboard, role-based access control, audit logging, and multi-node cluster orchestration—all behind an OpenAI-compatible API.
Lifeboat uses patent-pending optimizations including FP8 KV-cache compression, TurboQuant vector quantization, adaptive memory management, and fairness-aware scheduling to roughly double concurrent-session capacity on the same GPU hardware. These optimizations compress the cache while keeping model weights at full BF16 precision, preserving output quality.
Lifeboat deploys as a hardened Docker container or via a bare-metal installer. It supports air-gapped, on-premises, and private cloud environments with no external dependencies beyond NVIDIA GPUs and HuggingFace Hub for model downloads. All state is managed via an embedded SQLite database, enabling deployment in restricted and regulated environments.
Yes. Lifeboat exposes an OpenAI-compatible API, making it a drop-in replacement for existing OpenAI client code. It also supports Anthropic and Ollama protocol compatibility, working seamlessly with LangChain, LlamaIndex, LiteLLM, and other standard LLM client libraries.
Lifeboat provides four-tier role-based access control (RBAC), API key management with lifecycle controls, encryption at rest for secrets, comprehensive audit logging with CSV export, container hardening (non-root user, dropped capabilities, read-only filesystem), and optional TLS/HTTPS support. It's designed for self-hosted deployment to keep data within your environment.
Lifeboat supports 168+ model architectures through the underlying SGLang runtime. Because it's built as a non-invasive overlay touching only three upstream files, new model architectures from SGLang are absorbed automatically with a single upgrade command, ensuring broad and current model support.
Yes. Lifeboat includes multi-node cluster orchestration with a control-plane node and node agents on each GPU host. It features token-aware weighted load balancing, capacity gating, and intelligent routing across cluster nodes, all managed through a unified gateway and web dashboard.
Lifeboat provides a comprehensive web dashboard covering real-time metrics, server lifecycle management, a searchable model catalog with capability probing, cluster orchestration, user and API key management, configuration presets, and a full audit trail—turning inference operations into a point-and-click experience with no terminal required.