Fine-tune open-source models for real production use
Improve accuracy, reduce hallucinations, and control behavior — without managing training infrastructure.
Reliable infrastructure
Instant access to H100 and A100 clusters. We handle orchestration, recovery, and scaling automatically.
Research-driven performance
Leverage LEI Core and optimizers that reduce VRAM usage by 80% without sacrificing perplexity.
Universal model compatibility
Native support for the Hugging Face ecosystem. Import any model architecture with a single CLI command.
Frontier Model Support
Optimized for every use case.
- check_circleParameter-efficient training
- check_circleNo GPU fragmentation
- check_circleRapid deployment
LoRA Fine-tuning
Best for: Domain AdaptationLow-Rank Adaptation freezes the majority of model weights, training only a small set of injected matrices. This results in 10,000x smaller checkpoints and drastically lower compute costs while maintaining 98%+ performance.
Predictable, capped budget runs
Understand exactly how much a run will cost before a single GPU boots up. Set hard limits on node hours and enable preemptible instances to drop training costs substantially.
Throughput Efficiency
LEI Core reduces memory overhead per token, allowing for larger batch sizes on identical hardware compared to standard PyTorch implementations.
Context Parallelism
Our FFT Optimizer enables up to 25% larger context windows without increasing peak VRAM utilization during the backward pass.
Does fine-tuning make sense
for your team?
Model the real cost of per-employee AI subscriptions vs. a self-hosted fine-tuned model on EKS or AKS. Uses actual 2026 GPU pricing and vLLM throughput benchmarks.
| Method | Model | GPU setup | Est. cost |
|---|---|---|---|
| LoRA | 7B | 1× A10G · 1–3 hr | $5–$50 |
| QLoRA | 13B | 1× A100 · 2–5 hr | $15–$120 |
| LoRA | 13B–30B | 2–4× A100 · 4–12 hr | $200–$1,500 |
| LoRA | 70B | 4–8× H100 · 12–50 hr | $2K–$10K |
| FFT | 7B | 4–8× A100 · 1–5 days | $500–$2K |
| FFT | 13B | 8× A100 · 3–5 days | $2K–$8K |
| FFT | 70B+ | 8–32× H100 · weeks | $15K–$50K |
| Instance | GPU | On-demand/hr | req/sec | Best for |
|---|---|---|---|---|
| g5.xlarge | 1× A10G 24GB | $1.006 | ~50 | 7B |
| g5.2xlarge | 1× A10G 24GB | $1.212 | ~50 | 7–13B |
| p4d.24xlarge | 8× A100 320GB | $32.77 | ~30 | 13–70B |
| p5.48xlarge | 8× H100 640GB | $98.32 | ~10 | 70B+ |
| inf2.xlarge | 1× Inferentia2 | $0.758 | ~40 | 7B budget |
Ready to see the numbers for your specific stack?
Talk to an engineer arrow_forwardAdvanced capabilities.
Speculative Decoding
Fine-tune draft models specifically for speculative decoding to achieve 2-3x inference speedups on production endpoints.
Quantization-Aware
Integrated QAT ensures your model retains intelligence even when distilled to 4-bit or 2-bit weights.
RLHF & DPO
Full support for Reinforcement Learning from Human Feedback and Direct Preference Optimization for alignment and safety.
Your data never leaves your VPC.
Build the intelligence
your
business actually needs.
Start your first fine-tuning job in under 5 minutes. No infrastructure management required.