Serving Claude Code from DeepInfer on 4x B200

Running autonomous coding agents on your own hardware shifts the engineering bottleneck away from raw model weights and onto the infrastructure layer. When tools like Claude Code talk to an internal endpoint, the deciding factor between a snappy development experience and an unusable queue is managed by three critical components: a prefix cache, a request queue, and a strict GPU memory budget.

In this tutorial, we will examine how to configure, benchmark, and optimize a high-throughput inference setup using DeepInfer and a server equipped with four NVIDIA B200 GPUs. We will break down how to handle massive context windows, solve silent cache misses, and integrate with n1n.ai for seamless API aggregation and routing when scaling beyond single-node clusters.

Architecture Overview: The 4x B200 Setup

To understand the hardware limits, we deployed a benchmark workload simulating 100 concurrent coding agents. Each agent maintained a conversation context of approximately 450,000 tokens.

+-------------------------------------------------------+
|                    Claude Code                        |
+-------------------------------------------------------+
                           |
                           v
+-------------------------------------------------------+
|                    n1n.ai Gateway                     |
+-------------------------------------------------------+
                           |
                           v
+-------------------------------------------------------+
|                DeepInfer Engine (4x B200)             |
|   +-----------------------------------------------+   |
|   | GPU Tier (57M tokens / ~120 active sessions)  |   |
|   +-----------------------------------------------+   |
|   | Host RAM Tier (617M tokens / idle sessions)   |   |
|   +-----------------------------------------------+   |
+-------------------------------------------------------+

Without an aggressive caching strategy, reading 450K tokens on every step chokes even enterprise-grade hardware. By routing traffic through an optimized inference stack or leveraging reliable aggregation layers like n1n.ai, we can stabilize token throughput.

The Math of Prefix Caching

Coding agents resend the entire conversation history on every single step. Without a prefix cache, the inference engine must re-read hundreds of thousands of historical tokens before generating the next word.

• Raw Read Rate (No Cache): ~200,000 tokens per second across 4x B200 cards.
• Cached Read Rate (Two-Tier Cache): ~1,800,000 tokens per second (a 9x performance multiplier).
• Cache Hit Ratio: 96% of all tokens read by the model originated from the cache during sustained tests.

Implementing a two-tier caching hierarchy is essential:

1. GPU Memory Tier: Holds approximately 57 million tokens (roughly 120 concurrent 450K-token conversations).
2. Host RAM Tier: Holds approximately 617 million tokens, reserved for dormant or quiet sessions waiting for user input.

Avoiding Silent Cache Misses

One of the most insidious bugs encountered during deployment involved engine lifecycle synchronization. In our initial adapter configuration, running the engine across four ranks (one process per GPU), the system registered a 0% cache hit rate.

The adapter instructed the cache to drop entries the moment a final token was emitted. However, the inference engine only officially closes a sequence on the step after the final token. Consequently, every single request was re-read in full without throwing an explicit error.

The Quality Gate Verification Script

To catch silent misses before they degrade user experience, implement a deterministic validation check using temperature 0:

import requests
import json

def verify_prefix_cache(endpoint_url, api_key, document, query):
    headers = \{
        "Authorization": f"Bearer \{api_key\}