Chinese LLM Selection and Inference Deployment: A Practical Guide

Over the past few years, Chinese large language models (LLMs) have advanced rapidly. Several flagship models now deliver outstanding results across major benchmarks and open-source leaderboards, firmly establishing themselves in the industry’s top tier. For enterprises and developers, the abundance of strong Chinese models has made two questions central to AI architecture design: how to select a model scientifically, and how to bring it into production efficiently. This guide deliberately sets brand considerations aside and takes a technology-neutral look at the value of inference microservice platforms, the key dimensions of model selection, and best practices for enterprise-grade deployment.

Inference Microservice Platforms: The “Model Supermarket” of the AI Era

Before discussing how to choose a model, it is worth clarifying the core infrastructure behind large model adoption: the inference microservice platform.

As LLM parameter counts have leaped from the tens of billions to the hundreds of billions, traditional deployment approaches — such as spinning up containerized services directly on bare metal — face serious challenges: high GPU memory consumption, low concurrent throughput, and high inference latency. To solve these problems, major compute vendors have introduced inference microservice platforms. In plain terms, such a platform is a “model supermarket”: it wraps the underlying complexity — compute scheduling, GPU memory optimization, and inference engines built on techniques such as PagedAttention and Continuous Batching — into standardized API services.

In the past, the shelves of this “supermarket” were stocked mainly with well-known foreign open-source models. But as Chinese LLMs have improved across the board, more and more flagship Chinese models have quietly been integrated into these platforms, with directly callable API interfaces. This shift carries significant engineering value: developers no longer need to build a heavy inference environment from scratch. Through a unified platform interface, they can call top-tier Chinese models on demand, which greatly lowers both the cost of experimentation and the barrier to deployment.

Selecting a Chinese LLM: Core Evaluation Dimensions

Although several Chinese models rank highly on specific leaderboards, such as those covering web development, there is no single right answer to “which model to choose.” The decision must be based on a multi-dimensional evaluation against your actual business scenarios.

1. Task Scenario Fit

LLMs differ noticeably in their capability profiles, so the first principle of selection is scenario fit:

• Semantic understanding and dialogue. For intelligent customer service, knowledge base Q&A, and similar scenarios, focus on instruction-following ability, context window length, and hallucination rate. Some Chinese models excel at long-text parsing and can handle inputs of hundreds of thousands of characters, making them well suited for long-document summarization and in-depth conversations.
• Code generation and development assistance. For building intelligent coding tools, the model needs strong code completion, cross-file context understanding, and bug-fixing capabilities. Pay attention to its performance on code benchmarks such as HumanEval, as well as the depth of its support for your target programming languages.
• Logical reasoning and planning. In agent scenarios, the model must demonstrate strong chain-of-thought reasoning and tool-calling abilities.

2. Performance and Throughput Metrics

In enterprise scenarios with high concurrency, inference performance can matter more than a model’s absolute capability. Developers should track the following metrics:

• Time to First Token (TTFT). The key metric for user experience, especially in streaming conversations.
• Token generation rate. Determines the overall response time.
• Concurrent throughput. The number of requests per second the system can handle at cluster scale.

3. Deployment Cost and API Compatibility

Selection also needs to factor in the cost of calls. Models differ in parameter scale, so their per-call token pricing varies considerably. API compatibility is equally important. Favor models that support mainstream API formats (such as an OpenAI-style interface specification): when you switch the underlying model, only a small configuration change is needed in the application layer — no large-scale refactoring required.

Enterprise Inference Deployment Architecture and Best Practices

Once the selection direction is clear, how do you integrate these models into your business systems in a stable way? Below are two common deployment paths and their architectural practices.

1. Managed API Mode

For small and mid-sized teams, or for the early validation stage of a project, calling the managed API provided by the compute vendor is the most efficient option.

Architecture: Business backend -> API gateway -> inference microservice platform -> model response.

Core code example (calling an interface compatible with the standard format):

import requests
import json
# Store the API key in an environment variable or a secrets management service
API_URL = "https://api.inference-platform.com/v1/chat/completions"
API_KEY = "your_secure_api_key"
def call_llm_model(prompt, model_name="domestic-flagship-v1"):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    }
    payload = {
        "model": model_name,
        "messages": [
            {"role": "system", "content": "You are a professional technical architecture assistant."},
            {"role": "user", "content": prompt}
        ],
        "temperature": 0.7,
        "max_tokens": 1024,
        "stream": False
    }
    try:
        response = requests.post(API_URL, headers=headers, data=json.dumps(payload), timeout=30)
        response.raise_for_status()
        result = response.json()
        return result["choices"][0]["message"]["content"]
    except requests.exceptions.RequestException as e:
        # In production, connect this to a logging service and an alerting mechanism
        print(f"API request failed: {e}")
        return None
# Business call
answer = call_llm_model("Briefly describe the advantages of a microservice architecture.")

2. Cloud-Native Private Deployment Mode

For enterprises with strict data compliance requirements, or those that need to deeply customize their inference logic, private deployment on a cloud-native technology stack is the necessary path.

Key architecture points:

• Compute scheduling. Use a container platform such as Kubernetes to manage a GPU node pool, and scale pods elastically based on the length of the inference request queue.
• Storage and acceleration. Model weights can easily reach tens of gigabytes. Rely on high-performance object storage for weight management, and combine it with a distributed file system to mount the weights onto inference nodes.
• Traffic routing and load balancing. Deploy an API gateway at the entry point to handle load balancing, rate limiting and circuit breaking, and token-based billing statistics.

High availability:

In private deployment, a complete monitoring and alerting system is a must. Use a logging service to collect throughput logs from the inference engine, and monitor GPU memory usage, temperature, and compute utilization (SM utilization). When inference latency starts to jitter, you should be able to quickly determine whether the bottleneck is network I/O or compute saturation, and dynamically adjust batching parameters in response.

Conclusion

The rise of Chinese LLMs gives developers richer and more cost-effective choices. Whether you use ready-made APIs from an inference microservice platform — the “model supermarket” — or build a deep private deployment on your own cloud-native architecture, the core is to establish a standardized, pluggable evaluation and integration mechanism. Early in the selection process, build an automated evaluation set that reflects your real business scenarios, and let objective data guide the final model decision. Baidu AI Cloud also provides AI infrastructure and model services that can host a wide range of LLMs and serve their inference workloads, supporting enterprises through their AI transformation.