Iambic splits training and inference infrastructure
Lambda customer at Iambic Therapeutics on GPU infrastructure choices for ML training and inference
The hard part of AI infrastructure is that training compute and production inference are different jobs, and a company that tries to package both into one managed layer often ends up being too rigid for researchers and too incomplete for operators. The Iambic workflow splits them cleanly, with Lambda for reserved, high interconnect training clusters and AWS for reliable inference, which is exactly why a simpler DigitalOcean style GPU cloud can be more durable than an all in one stack.
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For training, the buyer wants direct control over cluster shape, interconnect, storage, and scheduling. Iambic chose Lambda because it could customize HGX and InfiniBand based clusters and price them below hyperscalers, while AWS and Oracle were either too expensive or not ready on interconnect quality.
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For inference, the value shifts from owning GPUs to operating a dependable service layer. In adjacent research, API first platforms like Fireworks are treated as a separate category from raw GPU clouds, because customers buy latency, concurrency, and reliability targets instead of infrastructure primitives.
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The market is already sorting into layers. Lambda, Fluidstack, and Together AI compete for startup and SMB workloads with speed and developer experience, while CoreWeave and Crusoe skew larger and more contract heavy. That makes the middle ground, raw enough for training but friendlier to use, the clearest white space.
The next winning GPU clouds are likely to add just enough software to make research clusters easier to use, while stopping short of turning themselves into full model deployment platforms. As training workloads spread across more vertical AI companies, the cleanest position is becoming the easiest place to rent serious multi GPU infrastructure without forcing customers into a single opinionated serving stack.