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AI DEPLOYMENT TOOLKIT

Local LLM Deployment Toolkit

Turn repetitive questions about vLLM commands, VRAM, costs, troubleshooting, and deployment selection into actionable tools. Use when deploying Qwen, Llama, DeepSeek, Ollama, GPUStack, and self-hosted API services.

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5

Core Tools

0

Backend Dependencies

1 min

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Choose the problem you want to solve

vLLM Command Generator Generate copy-paste commands by model, GPU, context length, and quantization. LLM VRAM Estimator Estimate weights, KV Cache, runtime overhead, and VRAM headroom. Local vs API Cost Calculator Compare monthly costs and payback period between self-hosted GPU and cloud API. Common Deployment Errors & Fixes Browse and search common deployment errors with root cause analysis and actionable fixes. Deployment Advisor Get recommendations for Ollama, vLLM, GPUStack, or cloud API based on your use case.
Usage Notes

How these tools are meant to be used

Why might estimates differ from actual runtime?

VRAM and throughput are affected by model architecture, attention implementation, batching strategy, quantization format, driver version, and vLLM parameters. The tools provide conservative pre-deployment estimates; always run load tests with target concurrency before going live.

Does the error diagnostic tool upload my logs?

No. All tools run entirely in your browser with no backend required, and no logs are sent to any server.

When should I choose a cloud API?

If your call volume is unpredictable, you lack GPU ops experience, or your model requirements change frequently, cloud APIs are usually more practical. Self-hosting fits stable workloads, privacy-sensitive scenarios, existing hardware, or cases with clear long-term cost pressure.