Why Your LLM API Is Slow in China Even With a Proxy (Cherry Studio, Claude Code, NVIDIA API Explained)

Many developers working with large language model (LLM) tools such as Cherry Studio, Claude Code, or OpenAI-compatible APIs (for example NVIDIA NIM endpoints) encounter a frustrating situation:

The API is reachable, but responses are slow—even after configuring a proxy.

This issue is often misunderstood as a simple “network problem” or “bad VPN configuration.” In reality, it is a multi-layer architectural issue involving routing, SDK behavior, and model inference latency.

This article breaks down the problem in a structured and practical way, focusing on real engineering behavior rather than assumptions.

1. How an LLM Request Actually Works

To understand latency, you must first understand the full request lifecycle.

A typical LLM request follows this path:

Client (Cherry Studio / Claude Code)
        ↓
Local network / system proxy
        ↓
API gateway (NVIDIA / OpenAI / third-party provider)
        ↓
Model inference cluster (GPU servers)
        ↓
Streaming token response

Latency can come from three independent layers:

Layer	Source of Delay	Optimizable
Local network → API	Routing, proxy, DNS	Yes
API → GPU cluster	Cross-border latency	Partially
GPU inference	Queueing, load, cold start	No

Most users incorrectly assume only the first layer matters.

2. Why “Proxy Configured but Still Slow” Happens

A very common misunderstanding is:

“If I set a proxy, all traffic goes through it.”

In practice, this is often not true.

2.1 Cherry Studio Proxy Behavior

In tools like Cherry Studio, proxy configuration may affect only:

UI rendering traffic
Plugin requests
Partial HTTP calls

But LLM inference requests may bypass system proxy settings.

This leads to a situation where:

UI traffic → Proxied ✔
LLM API calls → Direct connection ❌

As a result, users observe no improvement in latency.

2.2 Claude Code Network Behavior

Claude Code and similar CLI tools often rely on:

Anthropic SDK
OpenAI-compatible HTTP clients

However:

Not all SDK implementations fully respect system proxy variables.

This can result in:

base URL changes being ignored
direct network connections despite proxy configuration

2.3 System Proxy vs Application Proxy

Type	Coverage	Reliability
System VPN (Clash / TUN)	Global	Medium
HTTP_PROXY env variables	Partial	Unstable
Application-level proxy	Single app	Depends on implementation

Conclusion:

Only a controlled proxy layer ensures consistent routing behavior.

3. Why NVIDIA / LLM APIs Are Slow in China

Using NVIDIA’s API (e.g. integrate.api.nvidia.com) as an example, the request path typically looks like:

China → International gateway → US/EU data center → GPU cluster

This introduces several latency factors:

3.1 Cross-Border Network Distance

Higher RTT (round-trip time)
Packet loss risk
TLS handshake overhead

Even before inference starts, 100–300ms latency is common.

3.2 Time-to-First-Token (TTFT)

LLM latency is not only network-based.

Typical breakdown:

Stage	Latency
Connection setup	100–300ms
Queue waiting	0–2s
First token generation	200ms–2s

3.3 Model Load and Queueing

Popular models such as:

Minimax
Kimi
DeepSeek NIM

may experience:

request queueing
dynamic load balancing delays
peak-hour congestion

This causes inconsistent response speed.

4. Why Proxy Settings Often Do Not Work

Even when a proxy is configured, there are several failure points.

4.1 No Traffic Reaches the Proxy

This is the most common issue.

Symptoms:

Proxy logs show no API calls
Application behaves normally but bypasses proxy

Conclusion:

The application is not using the proxy at all.

4.2 Partial Proxy Coverage (DNS Only)

Some setups only proxy:

DNS resolution
but not TCP traffic

Result:

ping works
curl works partially
API remains slow

4.3 Wrong Proxy Location

Proxy server location matters significantly.

Location	Effectiveness
China-based VPS	No improvement
US-based VPS	Often worse
Singapore VPS	Optimal
Tokyo VPS	Optimal

5. Cherry Studio Request Architecture Issue

Cherry Studio separates:

UI layer
LLM API layer

This leads to a misleading situation:

UI traffic → Proxy ✔
LLM traffic → Direct ❌

How to verify this

You can confirm via:

checking proxy logs for /chat/completions
comparing curl latency vs app latency
using a local proxy endpoint test

6. Claude Code Network and Model Limitations

Claude Code introduces additional constraints:

6.1 Model Validation Layer

Even when using OpenAI-compatible APIs:

model names may be validated
unsupported models may be rejected

6.2 Base URL Not Fully Respected

Some internal calls may still:

bypass configured base URL
fall back to default endpoints

This leads to inconsistent routing behavior.

7. Recommended Architecture for Stable Performance

The most reliable solution is to introduce a unified proxy layer.

7.1 Architecture Overview

Client (Cherry Studio / Claude Code)
        ↓
Local proxy server (LiteLLM)
        ↓
Overseas VPS (Singapore / Japan)
        ↓
LLM API provider (NVIDIA / OpenAI-compatible)

7.2 Why LiteLLM Works Well

LiteLLM provides:

unified OpenAI-compatible interface
model routing abstraction
fallback mechanisms
consistent request handling

Example configuration

model_list:
  - model_name: unified-model
    litellm_params:
      model: openai/minimaxai/minimax-m2.7
      api_base: https://integrate.api.nvidia.com/v1
      api_key: YOUR_API_KEY

Start proxy server

litellm --config config.yaml --port 4000

Client configuration

Base URL: http://127.0.0.1:4000
Model: unified-model

7.3 Why This Architecture Works

This design ensures:

Problem	Solution
Inconsistent proxy usage	Single entry point
SDK bypass behavior	Forced routing
Multi-provider complexity	Unified abstraction

7.4 Importance of Region Selection

Proxy location strongly affects performance:

Region	Performance
Singapore	Best
Tokyo	Best
Hong Kong	Moderate
US	Not recommended

8. Systematic Debugging Checklist

When LLM requests are slow, follow this sequence:

Step 1: Confirm proxy usage

Check whether requests appear in proxy logs.

Step 2: Verify request path

Ensure:

Client → Proxy → API

is complete.

Step 3: Measure raw API latency

Use curl to isolate network issues.

Step 4: Check model inference delay

If latency varies significantly, it is likely queueing or load.

9. Frequently Asked Questions

Why is it still slow after setting a proxy?

Because LLM requests may bypass proxy settings entirely.

Why is curl fast but the app is slow?

The application is not using the proxy layer correctly.

Does VPN solve the problem?

Only if it forces full traffic tunneling, including API requests.

Why is NVIDIA API slow in China?

Due to cross-border routing and inference queueing.

What is the most stable solution?

A unified proxy layer with overseas routing (LiteLLM + VPS).

10. Summary

LLM API latency in China is not caused by a single factor. It is the combination of:

Cross-border network latency
Incomplete proxy implementation in tools
Model inference queueing and load

The most reliable solution is not switching VPNs, but building a controlled request routing layer that enforces consistency across all tools.

Why Your LLM API Is Slow in China Even With a Proxy: Cherry Studio, Claude Code, and NVIDIA API Fix