Sambanova AI InferenceをChatGPTとClaudeに接続

• 01 / 03構造化データを取得する
• 02 / 03埋め込みベクトルの生成方法
• 03 / 03オープンソースモデルの実行方法

最速日

748ms

最遅日

1136ms

14日間の傾向

遅延傾向+37%

SambaNova Connector

準備完了です。MCPクライアントを選択し、セットアップ手順に従ってください。

コネクタリンクhttps://edge.vinkius.com/vk_preview_z52tsMdkFaoCTCBeg6u7bbxNhhnjcT0r5KXU6hjj/mcpコピー

Claude Desktop

以下の手順で数秒で接続できます。

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — SambaNova capabilities are ready to use.

設定 · claude_desktop_config.jsonコピー

{
  "mcpServers": {
    "sambanova-ai-inference-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_z52tsMdkFaoCTCBeg6u7bbxNhhnjcT0r5KXU6hjj/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

各クライアントの手順ガイドはこちらです。 接続方法

Claude向けのガイド付きセットアップ？ SambaNovaをClaudeや他のAIに設定する

このConnectorが対応するすべてのAIクライアントを見る ↑

• 01

  AI Engineers

  They use this to build real-time applications that require high throughput.

• 02

  Developers

  They use this as a faster, more cost-effective alternative to standard LLM providers.

• 03

  Data Scientists

  They use this to generate embeddings for large-scale knowledge bases.

• 01

  Which models can I use with this MCP?

  You can access models like Meta-Llama-3.3-70B-Instruct, DeepSeek-V3.1, and MiniMax-M2.7.

• 02

  How do I connect it to my AI client?

  Once you subscribe on Vinkius, you can connect it to Claude, Cursor, Windsurf, or any other MCP-compatible client with one click.

• 03

  What makes the inference speed different?

  The speed comes from SambaNova's specialized SN40L chips and DataScale infrastructure, which are designed for high-speed dataflow.

• 04

  Can I use this for RAG?

  Yes, you can use the create_embedding capability to generate text representations for your retrieval-augmented generation workflows.

• 05

  Is this compatible with OpenAI capabilities?

  Yes, the create_chat_completion capability is compatible with the OpenAI Chat Completions API format.

• 06

  Which models are available for chat completions?

  You can use create_chat_completion with models like Meta-Llama-3.3-70B-Instruct, DeepSeek-V3.1, and MiniMax-M2.5 for high-speed text generation.

• 07

  Can I generate embeddings for my RAG pipeline?

  Yes! Use the create_embedding capability with models like E5-Mistral-7B-Instruct to create vectorized representations of your text data.

• 08

  What is the difference between create_chat_completion and create_response?

  create_chat_completion follows the standard OpenAI chat format, while create_response is a stateless API designed specifically for agentic workflows, returning typed output items.

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• 

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