Intelligent LLM Management System
The intelligence layer. Inference, training, orchestration.
ILM, the Intelligent LLM Management System, is the intelligence layer of the Think AI Fabric: hardware-aware orchestration engineered in symbiosis with think nodes, pre-installed on every one.
Every job placed against
what the silicon is actually doing.
ILM reads memory in use, thermal headroom and affinity from every device in the node, and places each job against what is true at that moment rather than what a nameplate claims. Across NVIDIA and Intel today, and the accelerators still to come.
Job manager
ILM ORCHESTRATOR
Placing every job
Telemetry
Placement log
Mixed silicon. Mixed AI workloads.
Zero idle compute.
Published measurement puts most AI clusters at 30 to 50 per cent utilisation, and inference silicon idle more than half the time. One model per chip is a convention, not a requirement. ILM places by what the silicon can actually hold, across NVIDIA and Intel today and the accelerators still to come, so models share an accelerator, large models shard across unequal ones, and training runs beside serving.
Four small models, four accelerators, and most of the VRAM doing nothing.
Illustrative consolidation on a four-chip node. Validated figures: 92.3% sustained utilisation, 0 to 3.5% overhead.
A closed API will answer your questions.
It will never learn your business.
A closed model is the same for you as it is for everyone else. You cannot train it on your contracts, your records or your language, because you never hold the weights. On the think platform, you can.
Three ways to teach it. All at once.
- SFT Supervised fine-tuning on your own examples
- LoRA Adapters trained against a frozen base
- QLoRA The same, with the base quantised to fit
Scheduled together, on the node that is already serving. No second estate to buy, no rewrite, and no new tooling to stand up: the engines your team already uses are the ones that run.
Down to the kernel.
Every layer, one team.
Six layers stand between a model and the silicon. think engineers five of them. The top stays open: any engine, any framework, no lock-in. Everything beneath it is tuned, orchestrated, and fused into one machine. By one team.
Bring any engine.
ILM speaks them all.
The engines your team already trusts, running side by side. No lock-in, no rewrites, no compromise.
One layer, not a pile of tools
Hardware-aware
ILM knows what every chip, thermal envelope, and watt can deliver, because it was engineered together with the hardware it runs. Scheduling decisions come from measured reality, not guesswork.
Whole workflow, one layer
Pre-training, fine-tuning, and inference on the same nodes, scheduled dynamically. No separate serving estate, no separate training estate, no idle handover between the two.
Sovereign and air-gapped by design
No egress. No external API dependency. ILM operates entirely inside your environment, so every model, every prompt, and every byte stays under your control.
Bonded to think hardware
ILM is licensed per node and ships pre-installed with a 1 year licence on every think AI node. It is not standalone software: the symbiosis with the hardware is where the efficiency comes from.
See ILM run your models.
Bring your model list. We will show you what a single node does with it.
Contact Think AI