SambaNova (AI Inference) MCP, Ready to Go
Connect your AI agents to SambaNova (AI Inference) for high-speed Llama 3 and DeepSeek performance in Claude or Cursor to speed up your production.
No credit card required. Experience the power of this integration risk-free.
Run Llama 3 and DeepSeek models with high-speed inference for production apps.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the SambaNova (AI Inference) MCP Server?
Average time for the server to become ready for requests over the last 13 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with SambaNova (AI Inference) 3 Tool Inference Setup
Use these tools to run high-speed chat completions, generate vector embeddings, and get structured agentic responses.
Create chat completion
Create a chat completion using SambaNova models. This tool works with the OpenAI Chat Completions API format for easy integration.
Create embedding
Create embeddings using SambaNova. It works on SambaStack to turn text into vectors for your search database.
Create response
Create a response using SambaNova Responses API. This tool returns typed output items specifically for agentic workflows.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,700+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
SambaNova (AI Inference) for High-Speed LLM Inference
This is for the AI engineer who's tired of high latency killing their production app or the data scientist who needs to process millions of embeddings without waiting all day.
AI Engineer
Building real-time apps that need low-latency inference and high throughput.
Backend Developer
Looking for a cost-effective, fast alternative to standard LLM providers for production.
Data Scientist
Generating embeddings for large-scale knowledge bases at scale.
Frequently Asked Questions
What models can I run with SambaNova (AI Inference)? +
You can run top-tier open-source models including Meta-Llama-3.3-70B-Instruct and DeepSeek-V3.1. This gives you high-performance options for various tasks.
Is SambaNova (AI Inference) fast enough for real-time apps? +
Yes, it's built on SN40L chips designed for record-breaking tokens-per-second. It's a great choice for low-latency requirements.
Can I use SambaNova (AI Inference) for my RAG system? +
Definitely. You can use the embedding tool to turn your documents into high-dimensional vectors quickly for your knowledge base.
Does SambaNova (AI Inference) support structured outputs? +
Yes, it features a specific tool for typed outputs. This is perfect for building agents that need to return specific data formats.
How do I connect SambaNova (AI Inference) to my AI client? +
Just subscribe to the MCP and add your SambaNova Cloud API key to your client. It works with Claude, Cursor, and others.
Is this MCP better than standard LLM providers? +
It depends on your needs. If you need high-speed inference on open-source models and lower latency, it's a strong choice.
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.
Can I generate embeddings for my RAG pipeline? +
Yes! Use the create_embedding tool with models like E5-Mistral-7B-Instruct to create vectorized representations of your text data.
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.
Your AI, connected to everything.
No credit card required · Free tier available
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