Enterprise AI Sovereignty with Private Parameter-Efficient Fine-tuning Pipelines
The math: base weight matrix W ∈ ℝ^(d×d), adapter matrices A ∈ ℝ^(d×r), B ∈ ℝ^(r×d). Frozen W, trainable AB. Memory footprint: r << d, so 2*(d×r) << d².
` Run `nix develop` → gives you CUDA 12.1 + PyTorch 2.1.1 instantly. No `pip install` drift across team machines. `
` Notice we **never** tokenize inside the data center; raw text stays private. Hugging Face datasets are **Arrow-backed**, so memory usage is low even for 1M rows. `
` Key safeguard: **Ray’s object store** pins tensor shards inside VPC. No external cloud store needed. `
` MinIO acts as **private S3**. Adapter diffs stay ≤ 10 MB, so bandwidth usage is negligible. `
` vLLM **merges** LoRA adapter at runtime. Endpoint returns **only inferences**, no weights. `
Note: PEFT RAM reflects **cached LoRA adapter only**. Base weights stay frozen on disk.
` Private data requires **extra vigilance**: no Hugging Face Hub safety checks.
` QLoRA trades **adapter precision** for throughput. Benchmark before committing.