Choosing a Chinese LLM: From Inference Serving to Engineering Best Practices

Introduction: The Evolution of LLM Inference Serving and the Selection Challenge

With the rapid iteration of open-source and closed-source large language models (LLMs), the challenge facing developers and enterprise architects has shifted from “how to train a model” to “how to efficiently integrate, deploy, and evaluate a wide range of LLMs.” In the past, inference microservice platforms released by well-known hardware vendors typically included only a limited selection of mainstream international open-source models. Recently, however, several flagship Chinese LLMs have quietly appeared on these inference platforms, allowing developers to call models from different vendors through unified API interfaces.

This trend marks a key step forward in the engineering maturity of Chinese LLMs. Yet with so many leading Chinese models now available, how should enterprises make objective selection decisions? What engineering practices should be followed when integrating inference services? This article sets aside brand bias and examines LLM selection strategies and engineering implementation from a technology-neutral perspective.

1. Moving Beyond Brand Perceptions: Scenario-Driven Model Evaluation

When choosing an LLM, developers often fall into the trap of relying solely on leaderboard rankings. While leaderboards focused on specific domains such as web development can provide useful reference points, the core factor that should drive model selection is the specific business scenario.

1. Code Generation and Web Development Scenarios

Recently updated Chinese flagship models have shown strong performance in code generation and frontend component construction. If the team’s primary goal is to assist with boilerplate code, API testing, or frontend page generation, the key evaluation criteria should be code completion accuracy, understanding of complex logic, and proficiency in specific programming language syntax. In practice, this can be tested by constructing complex coding prompts with multi-turn conversations and observing whether the model produces hallucinations or loses context.

2. Long-Context Processing and Logical Reasoning Scenarios

For scenarios involving large-scale document summarization, contract review, or deep data analysis, the model’s effective context window length and information extraction accuracy within long texts become critical evaluation metrics. Enterprises can run “needle in a haystack” tests by inserting specific information at different positions within very long texts and measuring the model’s recall rate.

3. General Conversation and Content Generation Scenarios

For customer-facing intelligent support or creative writing, instruction-following ability, natural tone, and coherence across multi-turn conversations matter more than raw benchmark scores. In these cases, human evaluation and preference alignment mechanisms often provide more value than automated testing.

2. Architectural Value and Integration Practices for Inference Microservice Platforms

More and more enterprises are choosing to access models through inference microservice platforms provided by hardware vendors or cloud providers rather than building local inference infrastructure from scratch. The main reason is that these platforms significantly reduce operational overhead. They typically offer containerized model services with automatic scaling and load balancing.

API Consistency and Smooth Migration

Inference platforms generally follow standard RESTful API or gRPC protocol specifications. This means that when enterprises run A/B tests or switch between models from different vendors, they do not need to significantly modify underlying business code. The following standardized model invocation example shows how to make requests through a unified interface:

import requests
import json
# Unified inference API endpoint provided by the platform
API_URL = "https://inference.example.com/v1/chat/completions"
# Replace with the actual platform API key
API_KEY = "your_platform_api_key"
def invoke_llm(model_name: str, messages: list):
    """
    Generic LLM invocation function
    :param model_name: Model identifier, such as "glm-5", "kimi-k2.5", etc.
    :param messages: Conversation history
    :return: Text response generated by the model
    """
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": model_name,
        "messages": messages,
        "temperature": 0.7,
        "max_tokens": 1024,
        "stream": False
    }
    response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
    if response.status_code == 200:
        result = response.json()
        return result.get("choices", [{}])[0].get("message", {}).get("content", "")
    else:
        raise Exception(f"API request failed: {response.status_code}, error: {response.text}")
# Example business invocation
chat_history = [
    {"role": "system", "content": "You are a senior backend architect."},
    {"role": "user", "content": "Design a high-concurrency message queue peak-shaving solution."}
]
# Developers can seamlessly switch model names for comparison testing
# For example: "enterprise_llm_v1" or "enterprise_llm_v2"
response_text = invoke_llm("enterprise_llm_v1", chat_history)
print(response_text)

With this abstraction layer, the business logic only needs to focus on constructing the messages payload, while inference scheduling, memory management, and other infrastructure concerns are handled entirely by the inference platform.

3. High-Availability Deployment Strategies in Cloud-Native Environments

When enterprises decide to deeply integrate LLM capabilities into core business workflows, relying solely on API calls may not satisfy extreme low-latency requirements or data privacy constraints. In such cases, hybrid deployment built on cloud-native infrastructure becomes the recommended approach. Using standard cloud service capabilities, enterprises can construct the following high-availability architecture:

1. Container Platforms and Elastic Scaling

LLM inference tasks typically involve high GPU memory usage and compute-intensive workloads. Through cloud-native container platforms, model inference services can be packaged as independent microservices. With HPA (Horizontal Pod Autoscaler) policies, services can automatically scale based on GPU utilization and request queue depth to handle traffic spikes.

2. Message Queues for Peak Shaving

For long-running generative tasks, synchronous API calls can easily lead to request timeouts or blocking. Enterprises should introduce message queues as a buffering layer. User requests first enter the message queue, backend inference workers consume them on demand, and results are pushed back to clients via webhooks or persistent connections after processing completes. This improves system throughput and ensures inference service stability.

3. End-to-End Monitoring and Logging

LLM outputs are inherently non-deterministic, making a comprehensive monitoring system essential. By integrating standard logging services and monitoring platforms, development teams should track the following key metrics in real time:

• TTFT (Time To First Token): First-token latency, which directly affects user experience.
• TPS (Tokens Per Second): Tokens generated per second, measuring model throughput efficiency.
• Error Rate and Retry Mechanism: Monitor API responses for 429 (rate limiting) or 500 (internal error) status codes, and configure exponential backoff retry logic.

4. Selection Recommendations and Summary

Given the current diversity of Chinese LLM offerings, enterprise model selection is not a single-choice decision but a systematic engineering evaluation. Developers and architects should follow these principles:

1. Scenario-Driven, Test-First: Leaderboards should serve only as a reference. Build a representative evaluation set tailored to your business and conduct blind tests and comparisons.
2. Embrace Standards, Decouple the Underlying Stack: Prioritize inference microservice platforms that support standard API protocols. Avoid tightly coupling business code to a single model, and preserve flexibility for smooth migration in the future.
3. Solid Infrastructure, Reliable Operations: An LLM’s capabilities depend not only on the algorithm itself but also on the robustness of the underlying compute scheduling and cloud-native infrastructure. Proper use of container platforms, message queues, and monitoring is essential for moving AI applications into production.

In summary, the arrival of Chinese flagship LLMs on inference platforms provides developers with a richer toolkit. Only by deeply integrating model capabilities with solid engineering practices can organizations fully unlock the productivity value of generative AI.