Frontier model
A model for everything
Frontier models handle a wide range of tasks. That range comes with a large compute footprint, and every request runs on it.
Your own model,built for your work.
We adapt an open model to your task using your data, or synthetic data we create, and test it on your real work before you accept it.
Your task does not need a frontier model
A smaller model trained on your job can do it just as well on far fewer GPUs, with faster responses and lower inference cost.
Forge
A model for your work
Forge trains a smaller open model on your task and tests it against your baseline. You get the quality you need on a much smaller GPU footprint.
How it works
Start from examples, opt-in traces, or just the task. We do the work, and your team approves the data and accepts or rejects each candidate.
Define
Agree the job, what a failure looks like, and what counts as success
Prepare
Turn approved and synthetic data into training and test sets, kept apart
Train
Train an open model on your prepared data until it handles the job
Evaluate
Compare it with what you use today, hard cases included
Deliver
Hand over the model and its evaluation once your team accepts it
Proven on your work before you switch
Every candidate is tested against your current model, on your own work. You see the results side by side, then decide.
Quality
As good or better at the job
Scored on your real cases, including ones it never saw in training, against the model you use today.
Speed and cost
Savings you can see
Response time, throughput, and the GPUs it needs, measured on the hardware it will run on.
Tradeoffs
Weak spots shown up front
If it slips anywhere outside the job, the report shows where, so nothing surprises you later.
Decision
You decide what ships
Accept it, ask for another round, or keep what you have. Nothing replaces your model without your approval.
The other ways to work with Sparset
Questions
Do we need traces or a dataset?
No. Your examples are enough to start, and if there is no data yet, we create synthetic data for the task.
Do you use our Stack or Edge traffic for training?
Only if you opt in, and only the sources you agree to.
Does custom mean training a model from scratch?
No. Forge adapts an existing open model to your task. Training a foundation model from scratch is not what Forge does.
Where does the model run?
Wherever you choose: on Stack, on Edge, or on your own setup.
How do we know the result is better?
By comparing it with what you use today on agreed tasks, including the hard cases and the serving constraints.
How long does it take and what does it cost?
It depends on the task, the data work, and the evaluation. Book a call and we walk through it against the job.
Bring the job. We will show you what Forge can build for it.
- 01Book a callTell us the task and what data you have.
- 02Set the barWe agree what the new model has to beat.
- 03Get your modelWe build and test it. You accept it if it clears the bar.