01
Supervised fine-tuning
Full-parameter and parameter-efficient fine-tuning, with configurable precision and gradient checkpointing.
Platform
Tuning and serving on the same cluster.
Most of the work in tuning a model is ordinary plumbing: jobs that die at hour nine, sweeps that run one after another when they could run at once, and a model that works in a notebook but not under load.
01 — Coverage
Four things that have to work.
The platform is the layer between your experiment and the cluster: it schedules the work, keeps it alive, and tells you what happened.
02
Reinforcement learning
Post-training with a separate rollout pool, so generation and learning do not compete for the same cards.
03
Parallel LoRA testing
Sweep many adapters across nodes at once instead of queueing them one after another.
04
Evaluation and checkpointing
Runs are checkpointed on an interval you set, and evaluated on a held-out set rather than on the training loss.
02 — Parallel LoRA
Sweeps that run in parallel.
When you are sweeping rank, learning rate and target modules, the wall-clock cost is not the compute — it is the queueing. Run the sweep as a single fan-out job across the cluster and the same work finishes in a fraction of the calendar time.
Each adapter keeps its own logs and checkpoints, so a failed arm does not take the sweep with it. When the sweep ends you get a comparable table, not a folder of directories to reconcile by hand.
In practice this is the change that shortens a two-week tuning cycle to a few days.
Sweep structure
Illustrative
- Base model
- Loaded once per node, shared across arms
- Fan-out
- One adapter per GPU, N arms per sweep
- Isolation
- Separate logs, metrics and checkpoints per arm
- Failure
- A failed arm is retried; the sweep continues
- Output
- Ranked comparison table across all arms
- Promotion
- Best arm exported for serving or further tuning
03 — Production
Packaging, serving and monitoring.
A notebook that produces a good answer is not a product. What we hand over runs as a service, with monitoring, a rollback path and a cost per call your finance team can see.
01
Package
Weights, tokeniser, preprocessing and inference code bundled into an image that builds the same way every time. No undocumented notebook cells holding the pipeline together.
02
Serve
An endpoint that autoscales on queue depth, batches where batching is free, and degrades predictably when traffic spikes instead of falling over.
03
Watch
Latency, error rate, throughput and cost per sample on one dashboard, with alerting that reaches a person. Model quality is monitored separately, on sampled real inputs.
04 — Operations
Data center build-out and GPU operations.
Some teams need to own the hardware — for cost at scale, for control, or because the data cannot leave the building. We build and run the facility so that owning it does not mean staffing it.
01
Build-out
Power, cooling, rack layout and network design for GPU-dense floors. Specified for the heat load you will actually run, not a generic reference design.
02
Commissioning
Procurement, acceptance testing and burn-in. A cluster is not delivered until it has run a real distributed job end to end.
03
Run it
Monitoring, firmware and driver lifecycle, spare-part handling and failure response. One accountable team for the floor and the fabric.
| Engagement | You provide | We provide |
|---|---|---|
| Advisory | Site, budget, target workload | Design, specification and procurement support |
| Build | Facility access and capital | Fit-out, commissioning, burn-in, handover |
| Operate | Your models and users | Monitoring, maintenance, escalation, capacity planning |
Where is the tuning work stuck?
Tell us what you are training and where it breaks — memory, throughput, evaluation, deployment. We will tell you which of these we can take off your hands.