OpenPipe

Fine-tune open LLMs faster with far less VRAM.

An open-source (Apache-2.0) framework for fine-tuning and running open-weight models with custom CUDA kernels — roughly 2x faster training and large VRAM savings, so 7B–13B models fit on a single consumer GPU. Free tier runs on Colab/Kaggle or locally; Pro and Enterprise tiers add multi-GPU and multi-node speedups. Exports to GGUF/Safetensors for llama.cpp, vLLM, and Ollama.

Hosted inference and fine-tuning for open-weights models.

Hosted inference and fine-tuning across hundreds of open-weights models (Llama, Mistral, DeepSeek, Qwen, etc.). Strong pricing for inference-at-scale; LoRA + full fine-tuning supported.

Fast inference + fine-tuning. Production deployments at scale.

Optimized inference platform for open-weights models with strong latency numbers and serverless + dedicated deployment options. Fine-tuning supported; vision and audio models alongside text.

Inference cloud for serving any AI model in production.

Production inference platform offering both pre-optimized Model APIs (Llama, DeepSeek, and more, billed per token) and dedicated GPU/CPU deployments for custom models, billed per minute with no charge for idle time. Custom models are packaged with its open-source Truss format and autoscale, including scale-to-zero. Aimed at low-latency, high-throughput serving.