You provide the fine-tuning data.
Minima does not supply missing proprietary training data.
Fine-tuning + optimization
Fit the model to your business. Then make it efficient.
You bring the fine-tuning data. Minima adapts the model to your task, then optimizes the resulting model and delivers it with the runtime for your infrastructure.
Explicit prerequisite
Your data is the starting point.
Task, dataset requirements and evaluation criteria are agreed before work starts.
Agree the task and dataset requirements.
We establish what the model must learn and whether the available data is suitable before work starts.
Define success before training.
Evaluation criteria and held-out evaluation data are agreed separately from the training set.
Workflow
Adapt first. Optimize second. Validate both.
- 01
Scope
Agree the task, base model, deployment hardware and success criteria.
- 02
Prepare
Review data suitability and separate training data from held-out evaluation sets.
- 03
Fine-tune
Adapt the model to the customer's task.
- 04
Optimize
Compress and prepare the fine-tuned model for efficient inference.
- 05
Validate and deploy
Evaluate task quality, serving performance and infrastructure requirements, then deliver the agreed weights and runtime.
Evaluation
Measure adaptation and optimization separately.
Fine-tuning quality improvements are not attributed to compression.
Fine-tuning comparison
Task benefit versus the starting model
Evaluate the adapted model against the starting model using the agreed task and held-out evaluation criteria.
Optimization comparison
Optimization benefit versus the fine-tuned model
Evaluate the optimized model against the fine-tuned model before optimization, including task quality and serving performance.
What you receive
The adapted deployment package.
- 01
Adapted and optimized model.
- 02
Agreed runtime.
- 03
Evaluation findings.
- 04
Deployment guidance.
Optimization capability
Qwen3.8-2.4T
Four NVIDIA B200 nodes → one NVIDIA B200 node
Four NVIDIA B200 nodes for the baseline become one NVIDIA B200 node with Minima.
4× smallerModel weights
3.5× smallerKV cache
2× higherTokens per second
This is an optimization result, not a fine-tuning experiment. It demonstrates Minima's optimization capability. Achievable results for a customer's fine-tuned model require its own evaluation.
Commercial model
Fine-tuning and optimization are scoped around your model and data.
Production deployment uses an annual software license priced on agreed GPU-hour usage.
Fine-tuning FAQ
Data, evaluation and deployment.
What data is required?
You provide the fine-tuning data. We agree the task and dataset requirements, review data suitability, and separate training data from held-out evaluation data before work starts.
Can we use an already fine-tuned model?
Yes. Minima can evaluate an already fine-tuned model as the starting point, subject to access to the necessary model artefacts.
How is quality measured?
Fine-tuning benefit is measured against the starting model using agreed task criteria. Optimization is evaluated separately against the fine-tuned model before optimization.
Is optimization-only available?
Yes. Explore the Enterprise optimization offer for optimization and deployment without fine-tuning.
Fine-tuning + optimization
Discuss fine-tuning
Tell us about your task, model, data readiness and deployment environment so Minima can scope the fine-tuning and optimization work.
Request received
Thank you. We’ll be in touch.
Your fine-tuning request has been accepted and sent to the Minima team.