250+ models across 80+ architectures, 15+ fine-tuning methods, 6 alignment algorithms. Fine-tune LLMs and vision-language models on managed infrastructure. No token counting, billed per second.
12
GPU Types
Entry to enterprise, 24-288 GB VRAM
250+
Models Supported
LLMs, VLMs, and MoE across 80+ architectures
15+
Fine-Tuning Methods
LoRA, QLoRA, full fine-tune, and more
3
Regions
US, EU, and Asia-Pacific
Why USF BIOS
Everything you need to train, align, and deploy custom AI models, from adapter methods to managed infrastructure, with per-second billing and transparent pricing.
Training Capabilities
Not just LoRA fine-tuning. Full-spectrum training from parameter-efficient adapters to preference alignment to continued pre-training, for both text and vision models.
SFT with 15+ fine-tuning methods: LoRA, QLoRA, AdaLoRA, LoHa, LoKr, BOFT, ReFT, VeRA, and full parameter training up to 1T.
DPO, SimPO, CPO, ORPO, KTO, and Reward Modeling. Align models to human preferences without building your own RL pipeline.
Fine-tune 120+ vision-language models: InternVL, Qwen-VL, LLaVA, DeepSeek-VL, MiniCPM-V, Phi Vision, and more.
Extend any foundation model with your proprietary data. Inject domain knowledge: medical, legal, financial, or code.
Deep Dive
15+ fine-tuning methods, 6 alignment algorithms, 250+ models across 80+ architectures. The technical depth your ML team needs, without the infrastructure overhead.
Parameter-efficient and full fine-tuning
From lightweight LoRA adapters to complete parameter updates. Pick the right method for your model size and budget.
Preference optimization at scale
Align models to human preferences without building your own RL pipeline. No separate reward model needed for most methods.
Online RL (PPO, GRPO, GKD) coming soon.
80+ architectures: LLMs, VLMs, and MoE
Every major open model family supported: text, vision-language, mixture-of-experts, reward models, and embeddings.
Across 80+ architectures, plus live Hugging Face Hub search.
Plan Your Training
Select your training goal and we show you the right adapters, data format, and supported models so you can plan before you start.
Each example contains an instruction and the expected model response. Supports single-turn and multi-turn conversations.
{"instruction": "Summarize this article",
"input": "The study found that...",
"output": "Researchers discovered..."}{"messages": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]}Why USF BIOS
We handle distributed training, GPU orchestration, checkpointing, and fault tolerance. Your team focuses on data quality and model performance.
From 1 to 8 GPUs per node. DeepSpeed and Megatron-LM built in, no cluster management needed.
Ulysses and ZigZag Ring Attention for long-context training efficiently across GPUs.
Training state saved to S3 automatically. Resume from any checkpoint if a job is interrupted.
Train in the region closest to your data. US, EU, and Asia-Pacific availability.
Each training job runs in its own GPU pod. No shared compute between users, your job gets the full GPU.
Go from raw dataset to production-ready model in hours, not months. No MLOps team required.
Platform Comparison
Honest, side-by-side comparison with the platforms you're evaluating. Every fact sourced from public documentation.
Swipe sideways to see every platform
| Capability | USF BIOS | Fireworks AI | Together AI | Tinker | SageMaker |
|---|---|---|---|---|---|
| SFT (LoRA) | DIY | ||||
| Full Fine-Tuning | - | DIY | |||
| VLM Training | 120+ models | Limited | - | Limited | DIY |
| Fine-Tuning Methods | 15+ | 2 | 2 | 1 | DIY |
| RLHF Algorithms | 6 (9 soon) | 3 | 1 | 3 | DIY |
| Continued Pre-Training | - | - | - | DIY | |
| Reward Modeling | - | - | - | DIY | |
| Megatron-LM | - | - | - | DIY | |
| Sequence Parallelism | Partial | - | - | DIY | |
| Models Supported | 250+ | ~30 | ~40 | 22 | Any |
| Model Architectures | 80+ | ~30 | ~40 | 22 | Any |
| Max Parameters | 1T | 1T | 100B | 550B | Any |
| Pricing Model | Per GPU-hour | Per GPU-hour | Per GPU-hour | Per GPU-hour | Per GPU-hour |
| Managed Infra | Partial | ||||
| Setup Required | None | None | None | None | Significant |
Data sourced from public documentation as of June 2026. "DIY" means the platform supports it if you build it yourself.
Pricing
12 GPU types, billed per second. No token counting, no method surcharges. Every training type (SFT, RLHF, CPT, VLM) uses the same rate.
Select your model and training configuration. We estimate memory with built-in headroom for optimizer states, evaluation passes, and peak memory spikes, so your run completes without interruption.
Sign up to see live pricing and availability.
How It Works
Built for teams where model quality and reliability are non-negotiable. Go from raw data to a deployable model, with full control over every training decision.
Upload your dataset or import directly from HuggingFace. Supports JSONL, Parquet, CSV, and image datasets for vision-language training.
Select from 250+ models, 15+ fine-tuning methods, and 6 alignment algorithms. Choose your GPU, set your hyperparameters. Every decision is yours.
Launch training on managed GPUs with automatic checkpointing and fault recovery. Monitor in real time. Export to HuggingFace or SafeTensors when ready.