Self-hosted LLMs, private inference, no per-token billing. NVIDIA T4, L4, and A100 options on Google Cloud. We respond with a quote in 24 hours.
Three tiers. Dedicated hardware, private inference, no data leaves your instance.
Mistral 7B, Llama 3 8B, Open WebUI BYO keys + local inference for small teams
Llama 3 70B (quantized), Mixtral 8x7B, faster inference, larger context windows
Production fine-tuning, large model serving (70B+), real-time inference at scale
Need something specific? Multi-GPU, A100 80GB, H100s? Just ask in the form.
Model, use case, expected throughput. Even a 2-sentence answer is fine — we'll figure out the rest.
A real quote: hardware tier, monthly cost, expected inference latency, time to provision. No drip campaigns, no back-and-forth sales calls.
Hardware, model runtime (Ollama / vLLM), Open WebUI wired in, DNS configured, SSL active. You log in and start chatting with your private LLM.
Monitor latency, swap models, scale GPU, troubleshoot. She's included free with every GPU plan.
Standard Open WebUI is API-only — you bring your own OpenAI / Anthropic / Gemini keys. With a GPU plan, we host open-source models (Llama, Mistral, etc.) on dedicated NVIDIA hardware in our infrastructure, with private inference and no per-token billing.
Yes. We support Llama 3, Mistral, Mixtral, Qwen, Gemma, and any Hugging Face OpenAI-compatible model. We help with download, quantization, and serving setup.
Most GPU builds are live within 5 business days. We provision the hardware, install the runtime (Ollama, vLLM, or your choice), wire up Open WebUI, and hand it over.
Yes. Inference runs on your dedicated GPU in our infrastructure. No data leaves your instance. No model telemetry. No per-token logging. Your models, your data.
Daisy is included. She can help you choose the right GPU tier, draft your use case, route you to a human for a quote, and (once provisioned) help you manage your models, monitor inference latency, and troubleshoot issues.
Multi-GPU is part of the Enterprise tier. We also do clustered inference (vLLM, Ray Serve) for high-throughput use cases. Talk to us.