> ## Documentation Index
> Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Compare GPU Pricing with AI Agents

> Use MCP tools to compare GPU types, pricing, and availability across regions — helping you pick the right hardware for your workload.

export const RuncrateStyles = () => {
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    const s = document.createElement('style');
    s.id = 'runcrate-overrides';
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      /* Match Runcrate's rounding scale (--radius: 0.75rem) */
      .rounded-sm { border-radius: 0.5rem !important; }   /* 8px */
      .rounded-md { border-radius: 0.625rem !important; } /* 10px */
      .rounded-lg { border-radius: 0.75rem !important; }  /* 12px */
      .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; }
      .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; }
      .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; }
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      .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; }

      /* Cards: never pure white in light mode */
      .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; }
      html.dark .card { background-color: #141414 !important; }

      /* Docs hero box */
      .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; }
      html.dark .rc-hero { background-color: #141414; border-color: #242424; }
      html.dark .rc-hero h1 { color: #f5f5f5; }

      /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */
      ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; }
      ::-webkit-scrollbar-track { background-color: transparent; }
      ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; }
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      *:hover::-webkit-scrollbar-thumb,
      *:focus::-webkit-scrollbar-thumb,
      *:active::-webkit-scrollbar-thumb { opacity: 1; }
      * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; }
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<RuncrateStyles />

Before deploying, ask your AI agent to compare GPU options. It can pull live pricing, filter by VRAM requirements, and recommend the best option for your workload.

***

## "Compare A100, H100, and RTX 4090 pricing. I need at least 40GB VRAM."

The agent queries available GPUs:

1. **`list_gpu_types`** — fetches all available GPU types with pricing and specs

> GPUs with 40GB+ VRAM:
>
> | GPU        | VRAM  | Price/hr  | Best for                             |
> | ---------- | ----- | --------- | ------------------------------------ |
> | RTX A6000  | 48 GB | \$0.60/hr | Development, small model fine-tuning |
> | A100 40 GB | 40 GB | \$1.20/hr | Training, mid-size inference         |
> | A100 80 GB | 80 GB | \$1.60/hr | Large model training, 70B inference  |
> | H100 80 GB | 80 GB | \$2.50/hr | Maximum throughput, 70B+ models      |
>
> **For your use case:**
>
> * If you're fine-tuning a 7B model, the **A6000** (\$0.60/hr) has enough VRAM and saves 75% vs H100.
> * If you're serving a 70B model, you need the **A100 80GB** minimum. The H100 gives \~40% more throughput for 56% more cost.
> * The RTX 4090 (24 GB) doesn't meet your 40GB requirement.

***

## "What's the cheapest GPU that can run Llama 3.1 70B?"

The agent calculates VRAM requirements and matches:

1. **`list_gpu_types`** — gets all GPU options

> Llama 3.1 70B needs \~70 GB VRAM at BF16, or \~35 GB at 8-bit quantization.
>
> **Cheapest single-GPU options:**
>
> | Option  | GPU        | VRAM  | Quantization          | Price/hr  |
> | ------- | ---------- | ----- | --------------------- | --------- |
> | Budget  | A100 80 GB | 80 GB | BF16 (full precision) | \$1.60/hr |
> | Premium | H100 80 GB | 80 GB | BF16 (full precision) | \$2.50/hr |
>
> **Multi-GPU option (lower per-GPU cost):**
>
> | Option | GPUs          | VRAM total | Price/hr  |
> | ------ | ------------- | ---------- | --------- |
> | Split  | 2x A100 40 GB | 80 GB      | \$2.40/hr |
>
> **Recommendation:** Single A100 80GB at $1.60/hr. It's the cheapest path to 70B serving with full precision. The 2x A100 40GB setup costs more ($2.40/hr) and adds tensor-parallel overhead.

***

## "Show me everything available under \$1/hr for quick experiments."

The agent filters by price:

1. **`list_gpu_types`** — gets all GPU types, filters by hourly rate

> GPUs under \$1/hr:
>
> | GPU       | VRAM  | Price/hr  | Good for                           |
> | --------- | ----- | --------- | ---------------------------------- |
> | RTX 4090  | 24 GB | \$0.35/hr | Dev work, 7-8B models, fine-tuning |
> | RTX A6000 | 48 GB | \$0.60/hr | Larger dev work, 13B models        |
> | RTX 3090  | 24 GB | \$0.25/hr | Budget experiments, prototyping    |
>
> **Best value for quick experiments:** RTX 4090 at \$0.35/hr. It has the best price-to-performance ratio for single-GPU workloads — fast enough for real training, cheap enough to leave running during iteration.
>
> A 4-hour experiment on RTX 4090 costs **\$1.40 total**.

***

## Tools used in this workflow

| Tool             | Purpose                                                      |
| ---------------- | ------------------------------------------------------------ |
| `list_gpu_types` | Fetch all available GPUs with pricing, VRAM, and region info |