On 5 October 2026, enterprise AI software company Iterate Studio Inc. launched Lifeboat, an inference engine for large language models with confidential computing built in. SiliconANGLE reported exclusively that the software claims to fit two to six times as many concurrent AI agent sessions on each graphics processing unit.
Lifeboat targets a memory and scheduling problem agents create at scale. A chatbot question usually triggers one model call; an agent working through a task can make dozens, and its context window grows with each step. Hundreds of agents on shared hardware fill the key-value cache quickly. Iterate.ai says standard inference engines can stall at four or five long-context requests at once.
Teams that hit that limit tend to buy more GPUs or move work to per-token cloud services, handing data to a third party. Brian Sathianathan, Iterate.ai’s co-founder and chief technology officer, said banks, insurers and health systems want to run agents on their own data “inside their own walls” without doubling GPU spend.
The approach starts with fair scheduling and admission control so each session gets its share of the GPU and a heavy document agent cannot crowd out interactive ones. Key-value cache optimisations double effective capacity while model weights stay at full precision, the company says. On mixture-of-experts models, only the experts a given agent needs are loaded. Every session runs inside its own security capsule with filtering, token budgets and sandboxed execution.
In company testing, a single Nvidia RTX PRO 6000 Blackwell running Qwen 30B-A3B held 2,048 concurrent sessions with Lifeboat and every request completed. The same engine with Lifeboat’s optimisations switched off topped out at half that number and managed 4,965 tokens per second against 8,714 with them on.
The widest gap showed up in a memory-pressure run of 128 sessions sending 18,000-token requests, in which Lifeboat kept 99th-percentile time to first token at 1.5 seconds. The baseline needed 189 seconds.
“Before any enterprise buys more GPUs for its agents, it should find out what the ones it already owns can do,” said Jon Nordmark, co-founder and chief executive of Iterate.ai. “A data center or neo-cloud that doubles concurrent sessions per card gets that capacity back without adding racks or power.”
Lifeboat’s top-tier Confidential Computing edition will not serve a request until hardware attestation passes. That check covers trusted execution features in AMD and Intel processors plus Nvidia’s confidential computing mode on the H100, B200, GB300 and other supported GPUs. Model weights are sealed inside the trusted execution environment and stay encrypted in use, whether the engine sits in a cloud confidential virtual machine or on customer-owned hardware.
Early access drew thousands of downloads within days, the company said, and the engine is generally available. A free Developer License covers non-commercial and evaluation use on up to two inference servers on a single node. Professional support moves to the Standard License at $49.99 per month. Attestation features sit in Confidential Computing at $499.99 per month; both commercial tiers offer a seven-day trial without a credit card.
Nordmark and Sathianathan discussed the company’s Generate platform and its NetApp partnership on theCUBE at NetApp’s Insight conference the week before launch, placing Lifeboat in a broader on-prem agent narrative.
For operators, the competitive pitch is density plus data sovereignty: more sessions per watt and per rack, with attestation when regulators or customers require it. Lifeboat does not invent new models; it packages the agent-inference bottleneck as infrastructure product.
Caveats: the 2,048-session figure and 2–6× density claim are vendor measurements on a specific MoE model. Buyers should reproduce tests with their own loads, context lengths and admission policies before signing annual contracts.
In a market where GPU spend for agents is rising faster than classic chatbot traffic, any real density gain eases pressure on HBM supply chains and power. Lifeboat positions itself against generic open-source engines and per-token clouds that do not bundle TEEs the same way.
Neural Pulse recommends CIOs compare Lifeboat with vLLM/TensorRT-LLM baselines on the same GPUs, measure p99 TTFT under long-context pressure, and demand attestation evidence in the target environment (cloud CVM versus on-prem).
In short, 5 October marks a concrete launch in the agent-density race: not a new model, but a runtime promising more concurrent sessions, fewer wasted tokens and encryption-in-use for regulated sectors.
