Crusoe Serverless Fine-Tuning

Tune it.
Deploy it.
Own it.

Iterate faster with Serverless Fine-Tuning.
No infrastructure wrangling. No surprise bills. Just breakthroughs.

Fine-tuning, simplified

01
Select your base model
Select your base model
Choose from a curated library of top models. Find the perfect fit for your specific use case, whether you need lightweight agility or frontier reasoning power.
02
Load your dataset
Load your dataset
Upload your training data to your isolated environment in standard JSONL or Parquet format.
03
Configure your fine-tuning job
Configure your fine-tuning job
Tune the settings with pre-configured defaults built on best practices. We run the LoRA (Low-Rank Adaptation) workflow for fast, precise, and cost-effective fine-tuning. Submit your job via the UI, SDK, or API.
04
Deploy or download your model
Deploy or download your model
Go live in one click using Self-Serve Deployments for inference in Crusoe Intelligence Foundry, or download your weights to deploy anywhere.

Choose from top LLMs to fine-tune

Model
Provider
Parameters
Context

DeepSeek V4 Flash

DeepSeek
DeepSeek
Parameters
158.1B
Context
1,048,576

Gemma 4 31B it

Google
Google
Parameters
32.7B
Context
262,144

GPT-OSS 120B

OpenAI
OpenAI
Parameters
120.4B
Context
131,072

GPT-OSS 20B

OpenAI
OpenAI
Parameters
21B
Context
128,000

Llama 3.3 70B Instruct

Meta
Meta
Parameters
70.6B
Context
131,072

Llama 3.1 8B Instruct

Meta
Meta
Parameters
8B
Context
128,000

Qwen3 235B A22B Instruct 2507

Alibaba
Alibaba
Parameters
235.1B
Context
262,144

Qwen3 8B

Alibaba
Alibaba
Parameters
8.2B
Context
32,000

Qwen3.5 9B

Alibaba
Alibaba
Parameters
9B
Context
256,000

Qwen3.5 2B

Alibaba
Alibaba
Parameters
2B
Context
256,000

Qwen3.6 35B A3B

Alibaba
Alibaba
Parameters
35B
Context
256,000

More discovery. Less drudgery.

Improve accuracy, fix outputs, and ship faster.

Features

AI-optimized infrastructure

Tune models reliably and cost-effectively on purpose-built AI infrastructure, without having to manage it. If a hardware blip occurs, the system automatically recovers and restarts, so downtime stays minimal.

Cost-effective fine-tuning

Only pay for what works. Token-based pricing tracks spend directly to model progress. Early stopping halts the job and the billing the moment your LLM stops improving.

No lock-in

Your model goes with you. Deploy in one click to Self-Serve Deployments for inference, or download raw weights in .safetensors format to use any other model deployment platform.

Full lineage, zero guesswork

Fine-tuned models carry full lineage from Crusoe Object Store back to the exact dataset, config, and eval that produced it. Any run is reproducible and any outcome is auditable.

Observability that travels

View detailed granular training metrics. Audit trails are included on every job.

Tune reliably. Scale seamlessly.

With Serverless Fine-Tuning in Crusoe Intelligence Foundry, you can quickly customize state of the art models on proprietary data, without provisioning clusters. Use Self-Serve Deployments for one-click inference — same platform, same data isolation, zero friction.

Our early experience with Crusoe's Serverless Fine Tuning product was seamless, and it worked like a charm. We look forward to leveraging it to optimize the latency and cost of our AI agents as we scale our infrastructure.
Dr. Will Leeney
AI Researcher
Dr. Hiskias Dingeto
AI Researcher

Resources

Resources to help you configure, launch, and evaluate fine-tuning jobs without rebuilding your stack.

Frequently
asked questions

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