LoRA and QLoRA on 70B-class models, no quantization tax.
Llama 3.3 70B, Mistral Large, Qwen 2.5 72B — load them in fp16 on a single Apple Silicon Studio and train adapters in MLX. A 10K-example LoRA finishes in an evening.
Talk to an engineer →Austin, Texas · Apple Silicon Training Cluster
Fine-tune LLMs on the largest unified memory in the cloud.
Per-hour pricing, US data residency, no spot-market roulette.
Frameworks we train with — and the models we train every week
What it's for
Three training jobs Apple Silicon now does as well as anything else.
Train smaller models end-to-end. Or continue pretraining one.
Mistral 7B, Qwen 2.5 7B, Phi-4, Gemma 2 9B — full supervised fine-tuning in MLX, including every parameter, every optimizer state, every gradient. 50K examples in well under an afternoon.
Talk to an engineer →DPO, ORPO, KTO — the pass after your SFT.
Preference-tuning loops on the same machine your SFT lived on. We maintain the MLX training recipes and the eval glue. You bring the preference pairs.
Send us a brief →Why Deliany
The cheapest way to fine-tune a 70B-class model. Honestly.
Unified memory at scale
Memory-bound runs above 32B don't have to be quantized or sharded. Apple Silicon's unified memory architecture lets the whole model live in one coherent address space — and the math gets very friendly very quickly.
MLX-native, recipes maintained
MLX ships with mixed precision, gradient checkpointing, distributed training, and PEFT helpers. We maintain the production recipes for every flow we run.
Direct engineering support
Every account gets a shared Slack Connect channel that routes to the engineers who maintain the recipes and own the fleet. No tier-1 scripts. No bots.
Transparent pricing
Hourly, monthly, or per-run. Quoted in plain English. No spot market. No commit minimums. No surprise invoices.
How it works
Two ways in. Both ship by Friday.
Rent the node, run your script.
Spin up an Apple Silicon node or a Studio. SSH in, run your MLX script, pull the adapter down when it's done. Resumable checkpoints and HuggingFace cache mounts standard.
Talk to an engineer →Send a dataset, get an adapter back.
Hand us a JSONL and a one-line training config (we have a starter for every flow). We run, monitor, eval on a held-out slice, and hand back a tested adapter with a brief writeup. Flat per-run pricing.
Send us a brief →Start a fine-tune today
Ready to try Apple Silicon for your next training run?
Pick a tier or send us a dataset. Self-serve nodes spin up in under an hour. Managed runs typically finish the same day.