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Your agents keep multiplying, but your GPU budget doesn't. Lifeboat fits 2–6x more concurrent agents on the same card at full model precision — no quantization, no quality loss. Confidential compute is built in (TEE attestation, weights encrypted in use), so sensitive data never leaves your perimeter. One engine from a DGX Spark on your desk to B300s in the rack. OpenAI-compatible, native ROCm, free dev license. Download it today: https://iterate.ai/lifeboat
Hey Product Hunt 👋 I'm Brian, co-founder at Iterate.ai.
We started building Lifeboat after watching the same wall get hit over and over: the moment you go from a chatbot to real agents, one user isn't one request anymore — it's dozens of chained calls hammering the GPU. Concurrency falls apart, latency spikes, and the usual answer is either "buy more GPUs" or "quantize the model and eat the quality hit." Neither felt right.
So Lifeboat took a different path:
Fit 2–6x more concurrent agents on the same GPU, at full precision — we don't quantize your weights to get the numbers up.
Keep throughput flat under heavy load instead of watching it collapse when everyone's agents fire at once.
Build confidential compute into the engine (TEE attestation, weights encrypted in use) so regulated teams can actually run this on sensitive data.
Run the same engine everywhere — a DGX Spark or AMD Strix Halo box on your desk up to B300s in the rack, native ROCm included.
A few things I'd genuinely love this community's take on:
Where's your agent stack actually bottlenecking today — raw tokens/sec, concurrency, cost per session, or data/compliance?
For those of you self-hosting: what made you pick vLLM/SGLang/Ollama/etc., and what would make you switch?
How much does full-precision-vs-quantized actually matter for your use case? Curious how many people quietly accept the quality hit.
Free dev license if you want to kick the tires locally: https://iterate.ai/lifeboat
I'll be in the comments all day — ask me anything, including the hard stuff on benchmarks and where we're still rough.
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About Lifeboat on Product Hunt
“Run 2–6x more agents on the GPU you already have”
Lifeboat was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #110 on the daily leaderboard. Your agents keep multiplying, but your GPU budget doesn't. Lifeboat fits 2–6x more concurrent agents on the same card at full model precision — no quantization, no quality loss. Confidential compute is built in (TEE attestation, weights encrypted in use), so sensitive data never leaves your perimeter. One engine from a DGX Spark on your desk to B300s in the rack. OpenAI-compatible, native ROCm, free dev license. Download it today: https://iterate.ai/lifeboat
Lifeboat was featured in SaaS (44.4k followers) and Artificial Intelligence (479.2k followers) on Product Hunt. Together, these topics include over 180k products, making this a competitive space to launch in.
Who hunted Lifeboat?
Lifeboat was hunted by Brian Sathianathan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Brian Sathianathan