Unsloth — Developer Use Cases

Local LLM fine-tuning: Run 4-bit or 16-bit LoRA fine-tuning jobs on a single consumer GPU with 60% less VRAM than standard approaches, scaling to multi-GPU (Pro) or multi-node (Enterprise) as workloads grow.
Agentic pipelines with local models: Expose locally running models via the OpenAI-compatible API to connect AI agents (e.g., built with Claude Code or Codex) to self-hosted inference endpoints, with self-healing tool calls for improved function-calling reliability.
Quantized model deployment: Export and serve models in GGUF or MLX formats for efficient local inference across Mac (Apple Silicon via MLX) and other platforms.
Secure remote model access: Use the Cloudflare tunnel integration to expose a local Unsloth instance over a secure tunnel for remote development or team access without cloud hosting.
Image and video generation: Run diffusion models locally for image and video generation workloads alongside text-based LLMs within the same environment.
High-throughput enterprise training: Leverage the Enterprise tier for 32x faster training, 5x faster inference, multi-node scaling, and up to 30% accuracy gains for production fine-tuning pipelines.