# SambaNova (AI Inference) MCP for AI Agents AI Agent Connect

> SambaNova (AI Inference) gives your AI agent access to high-speed inference on SambaNova's SN40L chips. It lets you run models like Llama 3.3-70B and DeepSeek-V3.1 with lower latency and higher throughput than standard providers. Use it for real-time chat, structured agentic outputs, or generating high-dimensional embeddings for RAG.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_z52tsMdkFaoCTCBeg6u7bbxNhhnjcT0r5KXU6hjj/ai-agent-connect
- **Tags:** llm-inference, llama3, deepseek, embeddings, high-performance-computing

## Description

You need your AI to think faster and handle more requests without breaking a sweat. This Connector connects your agent directly to SambaNova's infrastructure, giving it a direct line to some of the fastest open-source models available today. Instead of hitting a wall with slow response times or expensive per-token costs on standard platforms, you're getting high-performance inference on custom hardware. You can use it to power real-time chat apps where every millisecond counts, or to handle complex agentic tasks that require specific, typed data structures. If you're building a RAG system, it handles the heavy lifting of turning text into embeddings quickly. It's a solid choice for anyone who wants to move away from generic providers and get more control over their inference pipeline. You'll find it's a lot easier to scale your production apps when you have this kind of raw power behind your agent. It's one of the many high-quality integrations you'll find in the Vinkius catalog to keep your stack lean and fast. You get to choose from heavy hitters like Llama 3.3 and DeepSeek, ensuring your agent isn't limited by the hardware of a generic cloud provider. It's about getting the best possible tokens-per-second for your specific workload, whether that's high-volume batch processing or instant user interaction.

## Tools

### 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.

## Prompt Examples

**Prompt:** 
```
Explain how the SN40L chip architecture works using Llama 3.3.
```

**Response:** 
```
The SN40L chip uses a Reconfigurable Dataflow Architecture. This allows it to handle diverse AI workloads by reconfiguring its data paths on the fly. Key benefits include:

* High throughput
* Low latency
* Energy efficiency

It's designed specifically for high-speed inference.
```

**Prompt:** 
```
Generate an embedding for 'The company's main goal is sustainable growth'.
```

**Response:** 
```
I've generated that embedding for you. It's now ready to be stored in your vector database. It contains a 4096-dimension vector, which is perfect for your RAG search functionality.
```

**Prompt:** 
```
Process this history and give me a structured response using MiniMax.
```

**Response:** 
```
I've analyzed the conversation history. Here is the structured data you need for your agentic workflow:

* **User Intent**: Inquiry
* **Sentiment**: Positive
* **Action Required**: Provide pricing details
```

## Capabilities

### Run high-speed chat completions
Generate high-quality text responses using models like Llama 3.3-70B-Instruct at record speeds.

### Get structured agentic responses
Produce typed output items designed for complex agentic workflows instead of just raw text.

### Generate text embeddings
Turn large amounts of text into high-dimensional vectors for your RAG system quickly.

### Fine-tune model sampling
Control creativity and determinism using temperature, top_p, and top_k parameters.

### Access open-source models
Run Meta-Llama-3.3-70B-Instruct and DeepSeek-V3.1 on specialized SN40L hardware.

## Use Cases

### Real-time customer support
An agent uses create_chat_completion to provide instant answers to users without lag.

### RAG System Building
A developer uses create_embedding to index a massive library of technical manuals into a vector database.

### Agentic Workflows
A system uses create_response to parse complex instructions into structured JSON for downstream tasks.

### High-Throughput Batching
A data scientist processes thousands of product descriptions into embeddings for a search engine.

## Benefits

- Lower latency for real-time apps because SambaNova's SN40L chips are built for speed.
- Better control over agent outputs by using the create_response tool for typed data.
- Faster RAG setup by generating embeddings quickly with the create_embedding tool.
- Access to top-tier open-source models like Llama 3.3-70B-Instruct without the usual bottlenecks.
- Predictable costs and high throughput for scaling your AI-driven services.

## How It Works

The bottom line is you get high-speed inference from top models without the typical overhead of standard providers.

1. Subscribe to this Connector and grab your SambaNova Cloud API key.
2. Plug that key into your AI client's configuration.
3. Start querying models directly from Claude, Cursor, or your preferred agent.

## 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 Connector and add your SambaNova Cloud API key to your client. It works with Claude, Cursor, and others.

**Is this Connector 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.