Runtime Optimization & AI Economics

Private LLM inference that runs where your data lives.

Lifeboat gives enterprises an owned inference layer for production AI agents: higher concurrency on the same GPU hardware, no per-token cloud economics, and complete control over data, models, and hardware.
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6x

more concurrent capacity on the same GPU hardware

100%

request success at 2,048 simultaneous users

37%

more tokens per second at 128 concurrent sessions

True AI sovereignty

Private AI takes more than private access.

To own your AI future, you need control over the three things that matter most: your data, your models, and the hardware that processes every token.

Data

Your data stays where it belongs — inside your perimeter, governed by your policies, and never exposed to shared AI services.

Models

Use, tune, and control the models that power your workflows. Own what you build instead of depending on opaque external systems.

Hardware

Run inference on infrastructure you control, from private cloud to on-prem systems and edge deployments.

The enterprise AI challenge

Agents make cloud inference harder to secure and harder to budget.

Challenge 1

Security exposure grows with every inference request.

Agents process contracts, financials, patient records, customer data, and intellectual property. Every prompt can become an exposure point.
  • Data leakage through shared cloud models
  • Sensitive prompts stored on third-party servers
  • No hard isolation between inference requests
  • Compliance gaps across HIPAA, GDPR, PCI, SOC 2, and 21 CFR Part 11
Challenge 2

Token pricing turns agent growth into financial exposure.

One user question can trigger 5-10 LLM calls. At enterprise scale, always-on agents become autonomous token consumers.
  • Variable token costs are difficult to forecast
  • Agent chains multiply consumption across workflows
  • Cost attribution breaks across teams and agents
  • Scale increases budget risk, not just technical load
The LifeBoat answer

Own the inference layer that production AI depends on.

LifeBoat is an inference engine that improves token throughput and concurrency for enterprise LLM workloads through memory compression, scheduling, and model optimization.

01

KV cache optimization

Advanced memory compression removes redundant storage in the key-value cache that powers every LLM conversation.
40% less GPU memory for conversations, agents, and multi-turn workflows.
02

Intelligent scheduling

Fair resource scheduling keeps interactive requests responsive while adaptive admission control prevents cascading failures during peaks.
37% more tokens per second at 128 concurrent sessions.
03

MoE and quantization

Optimized pipelines improve efficiency for DeepSeek, Qwen, Mixtral, and other mixture-of-experts architectures.
Run powerful models on smaller hardware at a fraction of the cost.
Performance benchmarks

Measured head-to-head against SGLang on a single NVIDIA RTX PRO 6000 Blackwell GPU.

Benchmarks used a Qwen 30B A3B model and long-context workloads that simulate real enterprise agents processing large documents in parallel.

KV Cache Capacity

568K

tokens with LifeBoat vs. 284K with SGLang
Max concurrency

2,048

sessions at 100% request success
Throughput at 2,048

8,714

tokens per second vs. 4,965 with SGLang
p99 ttft at 128 Sessions

1.5s

vs. 189s with SGLang under pressure
Metric
LifeBoat
SGLang
Result
KV cache capacity
568K tokens
284K tokens
2.0x memory efficiency
Max concurrency at 100% success
2,048
1,024
2x more sessions
Throughput at 1,024 concurrent
9,611 tok/s
6,304 tok/s
52% faster
Throughput at 2,048 concurrent
8,714 tok/s
4,965 tok/s
76% faster
Success at 2,048 concurrent
2,048 / 2,048
1,282 / 2,048
76% faster
Memory pressure test

At 128 concurrent sessions, Lifeboat keeps responding.

In an 18K-token-per-request workload, SGLang's p99 time to first token reaches 189 seconds. LifeBoat holds p99 TTFT to 1.5 seconds, a 129x improvement.
Lifeboat
1.5s
SGLang
1.89s
Business impact

Turn token spend into infrastructure you control.

Eliminate per-token cloud costs

Every token processed on your own GPUs is a token you do not pay a cloud provider to run. Token costs become a capacity planning problem, not an unpredictable meter.

Maximize GPU utilization

Standard inference software can collapse at 4-5 concurrent long-context requests per GPU. LifeBoat is designed to handle 25+ on the same class of infrastructure.

Compete with cloud AI

Keep the OpenAI-compatible API, model choice, and developer experience teams expect, while retaining data sovereignty and compliance alignment.
Defense in depth

Private inference with governance built into the operating layer.

LifeBoat supports air-gapped deployment, role-based access control, JWT authentication, audit logging, browser-based operations, and multi-node clustering for production workloads.

  • Single Docker container
  • 168 model architectures
  • OpenAI-compatible API
  • 300+ node clustering
  • TP=1 to TP=8
Guardrails and content filtering
Sandboxed execution with no syscalls
Agent identity and least privilege
Observability and traffic governance
System-level firewall and whitelisting
Production-ready infrastructure

Built for the operational realities of enterprise AI.

Reliability under load

100% request success at 2,048 concurrent users, with auto-restart and orphan recovery for always-on availability.

Centralized operations

Browser-based dashboard, multi-GPU tensor parallelism, and multi-node clustering configured through the UI.

Security Capsule

Sub-millisecond per-stage latency, zero-copy memory isolation, inline token scanning, and immutable audit decisions.
Industry use cases

Private inference for sensitive, high-volume agentic workloads.

Healthcare

Clinical documentation, radiology reports, discharge summaries, drug discovery, and reasoning over patient records.

Financial services

Earnings analysis, SEC filing review, risk assessment, fraud detection, and auditable internal research.

Legal

Contract review, redlining, vendor risk analysis, and compliance monitoring with attorney-client confidentiality.

Manufacturing & energy

Equipment maintenance prediction, safety document analysis, and edge or private data center operational intelligence.

Government & defense

Air-gapped deployment for classified workloads with FedRAMP-aligned security architecture and no external API calls.
Ready to see LifeBoat in action?

Bring high-concurrency private inference to production AI.

See how LifeBoat performs against your models, workloads, and infrastructure.
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