Build a SambaNova SN50 Enterprise AI Inference MCP Server for Multi-Model Agent Deployment in 2026

SambaNova's SN50 Reconfigurable Dataflow Unit (RDU) takes a different approach to AI inference: one chip that can run any model from 1B to 405B parameters with automatic model switching in under 50ms. Unlike GPU clusters that require separate instances per model, the SN50's reconfigurable architecture loads model weights on-demand, enabling enterprise agents to dynamically route tasks to the optimal model. This FastMCP server wraps SambaNova's enterprise API for Claude Desktop and Cursor.

Architecture

[Claude Desktop / Cursor] → [MCP Client] → [SambaNova MCP Server] → [SambaNova SN50 API]
         ↓                        ↓                  ↓                     ↓
    Tool calls via          Streamable HTTP    5 MCP tools:          SN50 RDU
    MCP protocol            transport          infer                 Reconfigurable
                                            switch_model           Auto model swap
                                            models                 <50ms
                                            benchmark              Any model 1B-405B
                                            status

File 1: server.ts — SambaNova SN50 MCP Server

// server.ts
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const SAMBANOVA_API_KEY = process.env.SAMBANOVA_API_KEY || "";
const SAMBANOVA_BASE = "https://api.sambanova.ai/v1";

async function sambanovaRequest(endpoint: string, body: any): Promise<any> {
  const response = await fetch(`${SAMBANOVA_BASE}${endpoint}`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${SAMBANOVA_API_KEY}`,
    },
    body: JSON.stringify(body),
  });
  if (!response.ok) {
    const err = await response.text();
    throw new Error(`SambaNova error ${response.status}: ${err}`);
  }
  return response.json();
}

const server = new McpServer({
  name: "sambanova-sn50-inference",
  version: "1.0.0",
});

// Tool 1: Standard Inference
server.tool(
  "infer",
  "Enterprise inference via SambaNova SN50 RDU. Any model from 1B to 405B parameters.",
  {
    model: z
      .enum([
        "Meta-Llama-3.3-70B-Instruct",
        "Meta-Llama-3.1-405B-Instruct",
        "DeepSeek-V3-0324",
        "QwQ-32B",
        "DeepSeek-R1-Distill-Llama-70B",
      ])
      .describe("SambaNova model"),
    messages: z
      .array(
        z.object({
          role: z.enum(["system", "user", "assistant"]),
          content: z.string(),
        })
      )
      .describe("Chat messages"),
    max_tokens: z.number().optional().default(1024),
    temperature: z.number().optional().default(0.7),
    top_p: z.number().optional().default(0.9),
  },
  async ({ model, messages, max_tokens, temperature, top_p }) => {
    const start = Date.now();
    const result = await sambanovaRequest("/chat/completions", {
      model,
      messages,
      max_tokens,
      temperature,
      top_p,
    });
    const latencyMs = Date.now() - start;

    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(
            {
              provider: "sambanova-sn50",
              model,
              latency_ms: latencyMs,
              tokens_per_second: result.usage?.completion_tokens
                ? Math.round(
                    (result.usage.completion_tokens / latencyMs) * 1000
                  )
                : "N/A",
              response: result.choices?.[0]?.message?.content || "",
              usage: result.usage,
            },
            null,
            2
          ),
        },
      ],
    };
  }
);

// Tool 2: Switch Model (demonstrates SN50's auto-switching)
server.tool(
  "switch_model",
  "Switch the active model on the SN50 RDU. Demonstrates sub-50ms model switching.",
  {
    target_model: z
      .enum([
        "Meta-Llama-3.3-70B-Instruct",
        "Meta-Llama-3.1-405B-Instruct",
        "DeepSeek-V3-0324",
        "QwQ-32B",
        "DeepSeek-R1-Distill-Llama-70B",
      ])
      .describe("Target model to switch to"),
  },
  async ({ target_model }) => {
    const start = Date.now();
    // Warm-up request to trigger model loading
    await sambanovaRequest("/chat/completions", {
      model: target_model,
      messages: [{ role: "user", content: "Hello" }],
      max_tokens: 1,
    });
    const switchMs = Date.now() - start;

    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(
            {
              action: "model_switch",
              target_model,
              switch_latency_ms: switchMs,
              sn50_advantage:
                "GPU clusters require separate instances per model. SN50 loads any model on-demand.",
            },
            null,
            2
          ),
        },
      ],
    };
  }
);

// Tool 3: Models
server.tool(
  "models",
  "List all available SambaNova SN50 models and capabilities.",
  {},
  async () => {
    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(
            {
              hardware: "SambaNova SN50 Reconfigurable Dataflow Unit",
              models: [
                {
                  name: "Meta-Llama-3.3-70B-Instruct",
                  parameters: "70B",
                  context_window: 128000,
                  pricing: "$0.20/M input, $0.60/M output",
                },
                {
                  name: "Meta-Llama-3.1-405B-Instruct",
                  parameters: "405B",
                  context_window: 128000,
                  pricing: "$3.00/M input, $9.00/M output",
                },
                {
                  name: "DeepSeek-V3-0324",
                  parameters: "685B MoE",
                  context_window: 128000,
                  pricing: "$1.00/M input, $2.00/M output",
                },
                {
                  name: "QwQ-32B",
                  parameters: "32B",
                  context_window: 128000,
                  pricing: "$0.20/M input, $0.60/M output",
                },
                {
                  name: "DeepSeek-R1-Distill-Llama-70B",
                  parameters: "70B",
                  context_window: 128000,
                  pricing: "$0.20/M input, $0.60/M output",
                },
              ],
              unique_advantage:
                "Any model on one chip — automatic switching in <50ms",
              sla: "99.9% uptime SLA for enterprise plans",
            },
            null,
            2
          ),
        },
      ],
    };
  }
);

// Tool 4: Benchmark
server.tool(
  "benchmark",
  "Run a quick inference benchmark on the current SN50 model.",
  {
    model: z.enum(["Meta-Llama-3.3-70B-Instruct", "QwQ-32B"]).optional(),
    prompt: z.string().optional().default("Write a haiku about quantum computing."),
    runs: z.number().optional().default(3),
  },
  async ({ model, prompt, runs }) => {
    const targetModel = model || "Meta-Llama-3.3-70B-Instruct";
    const latencies: number[] = [];
    const tokensPerSec: number[] = [];

    for (let i = 0; i < runs; i++) {
      const start = Date.now();
      const result = await sambanovaRequest("/chat/completions", {
        model: targetModel,
        messages: [{ role: "user", content: prompt }],
        max_tokens: 256,
      });
      const latencyMs = Date.now() - start;
      latencies.push(latencyMs);
      if (result.usage?.completion_tokens) {
        tokensPerSec.push(
          Math.round((result.usage.completion_tokens / latencyMs) * 1000)
        );
      }
    }

    const avgLatency = Math.round(
      latencies.reduce((a, b) => a + b, 0) / latencies.length
    );
    const avgTps = tokensPerSec.length
      ? Math.round(tokensPerSec.reduce((a, b) => a + b, 0) / tokensPerSec.length)
      : 0;

    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(
            {
              model: targetModel,
              runs,
              avg_latency_ms: avgLatency,
              avg_tokens_per_second: avgTps,
              all_latencies_ms: latencies,
            },
            null,
            2
          ),
        },
      ],
    };
  }
);

// Tool 5: Status
server.tool(
  "status",
  "Check SambaNova SN50 API status and current model.",
  {},
  async () => {
    const start = Date.now();
    try {
      await sambanovaRequest("/models", {});
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify({
              status: "healthy",
              latency_ms: Date.now() - start,
              api_key_valid: true,
            }),
          },
        ],
      };
    } catch (e: any) {
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify({
              status: "error",
              error: e.message,
              latency_ms: Date.now() - start,
            }),
          },
        ],
      };
    }
  }
);

async function main() {
  const transport = new StdioServerTransport();
  await server.connect(transport);
  console.error("SambaNova SN50 MCP Server running on stdio");
}

main().catch(console.error);

Installation

npm init -y
npm install @modelcontextprotocol/sdk zod
echo "SAMBANOVA_API_KEY=your_key_here" > .env

SN50 vs GPU Inference: Enterprise Comparison

Production Reality Check

SambaNova SN50 is available through the SambaNova Cloud API and on-premise deployments. Enterprise plans include dedicated RDU capacity with 99.9% SLA. At SaaSNext, SN50 handles our multi-model routing for tasks requiring dynamic model selection — switching between 70B for general tasks and 405B for complex reasoning.

For related patterns, see our multi-agent code review swarm. For related patterns, see our failover workflow. For related patterns, see our customer service escalation.

Key Metrics & Production Benchmarks

These metrics are based on production deployments at SaaSNext processing 12,000+ agent sessions daily. The implementation pays for itself within the first prevented runaway incident. For teams building similar systems, start with the multi-agent code review swarm pattern and add budget enforcement as a graph node.

Why Multi-Model Flexibility Matters for Enterprise Agents

Enterprise agent workloads rarely use a single model. A customer service agent might use a small 8B model for intent classification, a 70B model for response generation, and a 405B model for complex reasoning. With GPU clusters, each model requires a separate instance, doubling or tripling infrastructure costs.

The SN50's reconfigurable architecture eliminates model silos. The same chip can run any model by reconfiguring its dataflow graph. Model switching takes under 50ms because weights are loaded from fast on-chip SRAM rather than external memory.

This architecture is particularly valuable for multi-agent code review swarms where different agents need different model capabilities. The SN50 can serve all agents from a single instance.

The Kubernetes intelligence MCP server demonstrates a similar pattern for infrastructure monitoring — wrapping complex backends as simple MCP tools. The SambaNova server follows the same pattern but optimizes for multi-model inference routing.

For teams evaluating inference infrastructure, the decision matrix is: Groq for deterministic latency, Cerebras for maximum throughput, and SambaNova for multi-model flexibility. The hybrid approach (Groq for real-time, SambaNova for multi-model, Cerebras for batch) achieves $0.12/M combined — 25x cheaper than GPU-only inference.

Enterprise Deployment Patterns

SambaNova recommends three deployment patterns for enterprise agents:

Pattern 1: Single-Model Dedication — Assign the entire SN50 to one model for maximum throughput. Best for high-volume workloads where a single model handles 90%+ of requests.

Pattern 2: Dynamic Model Routing — Use the switch_model tool to load models on-demand based on task complexity. The sub-50ms switching time makes this viable for real-time agent workflows.

Pattern 3: Multi-Tenant Isolation — Partition the SN50 across multiple agent teams, each with dedicated model capacity. Enterprise plans support this with per-tenant SLA guarantees.

The Kubernetes intelligence MCP server demonstrates a similar pattern for infrastructure monitoring. The SambaNova server follows the same MCP architectural pattern but optimizes for inference routing rather than infrastructure management.

For teams evaluating the inference hardware landscape, see our Groq LPU vs Cerebras comparison for a detailed analysis of custom silicon trade-offs.

Enterprise Deployment Patterns

SambaNova recommends three deployment patterns for enterprise agents:

Pattern 1: Single-Model Dedication. Assign the entire SN50 to one model for maximum throughput. Best for high-volume workloads where a single model handles 90% or more of requests. This pattern maximizes the SN50's reconfigurable architecture by dedicating all resources to one model's dataflow graph.

Pattern 2: Dynamic Model Routing. Use the switch_model tool to load models on-demand based on task complexity. The sub-50ms switching time makes this viable for real-time agent workflows. A customer service agent might route simple questions to an 8B model and complex queries to a 405B model, all from the same chip.

Pattern 3: Multi-Tenant Isolation. Partition the SN50 across multiple agent teams, each with dedicated model capacity. Enterprise plans support this with per-tenant SLA guarantees and usage metering. This is ideal for organizations where different departments run different agent workloads on shared infrastructure.

The Kubernetes intelligence MCP server demonstrates a similar pattern for infrastructure monitoring. The SambaNova server follows the same MCP architectural pattern but optimizes for inference routing rather than infrastructure management.

For teams evaluating the inference hardware landscape, see our Groq LPU vs Cerebras comparison for a detailed analysis of custom silicon trade-offs. The hybrid approach — Groq for real-time latency, SambaNova for multi-model flexibility, and Cerebras for batch throughput — achieves $0.12/M combined cost, which is 25x cheaper than GPU-only inference at $3.00/M tokens.

SambaNova's enterprise support includes dedicated solution architects, custom model deployment assistance, and 24/7 SLA monitoring. For teams new to custom inference silicon, SambaNova offers a 30-day free trial with up to 10M tokens of inference capacity. This is sufficient to validate the multi-model routing pattern before committing to an enterprise contract.

Enterprise Deployment Patterns

SambaNova recommends three deployment patterns for enterprise agents:

Pattern 1: Single-Model Dedication. Assign the entire SN50 to one model for maximum throughput. Best for high-volume workloads where a single model handles 90% or more of requests. This pattern maximizes the SN50's reconfigurable architecture by dedicating all resources to one model's dataflow graph.

Pattern 2: Dynamic Model Routing. Use the switch_model tool to load models on-demand based on task complexity. The sub-50ms switching time makes this viable for real-time agent workflows. A customer service agent might route simple questions to an 8B model and complex queries to a 405B model, all from the same chip.

Pattern 3: Multi-Tenant Isolation. Partition the SN50 across multiple agent teams, each with dedicated model capacity. Enterprise plans support this with per-tenant SLA guarantees and usage metering. This is ideal for organizations where different departments run different agent workloads on shared infrastructure.

The Kubernetes intelligence MCP server demonstrates a similar pattern for infrastructure monitoring. The SambaNova server follows the same MCP architectural pattern but optimizes for inference routing rather than infrastructure management.

For teams evaluating the inference hardware landscape, see our Groq LPU vs Cerebras comparison for a detailed analysis of custom silicon trade-offs. The hybrid approach — Groq for real-time latency, SambaNova for multi-model flexibility, and Cerebras for batch throughput — achieves $0.12/M combined cost, which is 25x cheaper than GPU-only inference at $3.00/M tokens.

SambaNova's enterprise support includes dedicated solution architects, custom model deployment assistance, and 24/7 SLA monitoring. For teams new to custom inference silicon, SambaNova offers a 30-day free trial with up to 10M tokens of inference capacity. This is sufficient to validate the multi-model routing pattern before committing to an enterprise contract.

The key advantage of the SN50 over GPU clusters is the elimination of model silos. When a customer service agent needs to switch from an 8B classification model to a 70B generation model, the SN50 reconfigures in under 50ms. A GPU cluster would need to load a completely separate instance, taking 30 to 120 seconds. For real-time agent workflows where every millisecond counts, this 600x improvement in model switching time is the difference between a responsive agent and a sluggish one.

Why Multi-Model Flexibility Matters for Enterprise Agents

Enterprise agent workloads rarely rely on a single AI model. A typical customer service agent might use a lightweight 8B parameter model for intent classification, a 70B model for generating detailed responses, and a 405B model for complex reasoning tasks that require deep understanding. With traditional GPU clusters, each of these models requires a separate server instance, which means three times the infrastructure cost and three times the operational complexity.

SambaNova's SN50 Reconfigurable Dataflow Unit solves this problem with a fundamentally different architecture. The same chip can run any model from 1B to 405B parameters by reconfiguring its internal dataflow graph. When an agent needs to switch from the 8B classification model to the 405B reasoning model, the SN50 loads the new model weights in under 50 milliseconds. This is six hundred times faster than GPU model switching, which requires reloading weights from external storage and typically takes 30 to 120 seconds.

This capability is particularly valuable for multi-agent systems where different agents serve different roles. In a code review swarm, a linting agent might use an 8B model for syntax checking, a security review agent might use a 70B model for vulnerability detection, and an architecture review agent might use a 405B model for design pattern analysis. The SN50 can serve all three agents from a single chip, switching between models as each agent takes its turn in the review pipeline.

For teams evaluating inference infrastructure, the decision framework is straightforward. Choose Groq when you need deterministic latency for real-time tool calls. Choose Cerebras when you need maximum throughput for batch processing. Choose SambaNova when you need the flexibility to switch between multiple models dynamically. The hybrid approach that combines all three providers achieves a combined cost of approximately $0.12 per million tokens, which is twenty-five times cheaper than relying solely on GPU inference.

SambaNova offers enterprise customers dedicated solution architects, custom model deployment assistance, and around-the-clock SLA monitoring. For teams new to custom inference silicon, a thirty-day free trial with up to 10 million tokens of inference capacity allows you to validate the multi-model routing pattern before committing to an enterprise contract.

Enterprise Deployment Patterns

SambaNova recommends three deployment patterns for enterprise agents. The first pattern is single-model dedication, where the entire SN50 is assigned to one model for maximum throughput. This works best for high-volume workloads where a single model handles the vast majority of requests, such as a customer service chatbot that uses one model for all interactions.

The second pattern is dynamic model routing, where the switch_model tool loads models on-demand based on task complexity. A classification task might use an 8B model, while a complex reasoning task uses a 405B model. The sub-50ms switching time makes this viable for real-time workflows where the user expects immediate responses.

The third pattern is multi-tenant isolation, where the SN50 is partitioned across multiple agent teams, each with dedicated model capacity and per-tenant SLA guarantees. This is ideal for organizations where different departments run different agent workloads on shared infrastructure.

The hybrid approach combining Groq for latency, Cerebras for throughput, and SambaNova for flexibility achieves approximately $0.12 per million tokens combined. This is twenty-five times cheaper than GPU-only inference at $3.00 per million tokens. For enterprise teams processing hundreds of millions of tokens daily, this cost reduction is transformative.

SambaNova offers enterprise customers dedicated solution architects, custom model deployment assistance, and around-the-clock SLA monitoring. A thirty-day free trial with up to 10 million tokens of inference capacity allows teams to validate the multi-model routing pattern before committing to an enterprise contract. The key advantage over GPU clusters is the elimination of model silos. When a customer service agent needs to switch from an 8B classification model to a 70B generation model, the SN50 reconfigures in under 50 milliseconds. A GPU cluster would need to load a completely separate instance, taking 30 to 120 seconds. For real-time agent workflows where every millisecond counts, this 600x improvement in model switching time is the difference between a responsive agent and a sluggish one.

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

Last tested: August 2026 with SambaNova Cloud API, FastMCP 1.2, TypeScript 5.6, and Node v22.