Domestic LLM Inference Serving: A Practical Selection Guide from Capability Evaluation to Deployment

1. Separate model selection from leaderboard chasing

Large language models are being released at an increasingly rapid pace, and public leaderboards have proliferated. Many developers start by checking rankings and picking the highest-scoring model. That approach has some value when information is limited, but once you enter the engineering selection phase, relying on rankings alone is rarely sufficient.

There are three main reasons.

First, leaderboards are usually based on fixed datasets and fixed tasks. They cannot cover private data, domain knowledge, long-tail tasks, or multi-turn interactions in real business scenarios. A model that performs well on public benchmarks will not necessarily perform better on your customer service conversations, contract parsing, code generation, or data analysis tasks.

Second, model capability is only one part of the system. Even when model quality is similar, inference performance, time to first token, concurrent throughput, context length support, structured output stability, tool-calling consistency, deployment cost, and operational complexity all significantly affect the final experience.

Third, business goals are layered. Some scenarios require strong reasoning, some require low latency, some require strict format compliance, and some require frequent external tool calls. Forcing all of these requirements onto a single “strongest model” is often less effective than configuring different models for different scenarios.

A more practical approach is to build your own evaluation framework first, then use small-sample testing and real business validation to filter candidates. Leaderboards can serve as an initial screening signal, but should not be the final decision criterion.

2. A reusable model selection framework

Technical teams can evaluate whether a candidate model deserves further testing across five dimensions.

2.1 Base capability and task fit

This step focuses on whether the model can actually do the job. Start with a labeled internal test set covering three categories of tasks:

• Generation: summarization, expansion, rewriting, code generation, copy generation
• Understanding: classification, extraction, sentiment analysis, intent recognition
• Interaction: multi-turn dialogue, tool selection, parameter filling, result explanation

Do not only check whether the final answer is correct. Also observe whether the model follows instructions consistently, whether it loses key information in long contexts, and whether it behaves inconsistently across turns. For scenarios that require strict output formats, measure pass rate using schema validation rather than manual spot checks.

2.2 Inference performance and resource consumption

When model capabilities are close, inference performance often becomes the deciding factor. Key metrics include:

• Time to first token: the time from when a user sends a request to when the first token is returned
• Generation speed: tokens generated per second
• End-to-end latency: the total time required to return a complete response
• Concurrent throughput: the number of requests the system can sustain under fixed hardware conditions
• Context utilization efficiency: whether performance degrades noticeably in long-text scenarios

The same model can perform very differently on different inference platforms. Therefore, performance evaluation should be conducted in the candidate deployment environment rather than relying solely on public reports.

2.3 Deployment form and controllability

Enterprise users typically have more complex deployment requirements than individual developers. Common forms include:

• Managed cloud API access: fast onboarding and low operational burden, but data must leave your environment
• Private deployment: data remains under your control, but requires hardware investment and operational capability
• Hybrid deployment: core data is processed locally, while non-sensitive tasks use cloud services

If the business involves finance, healthcare, government, or other strongly regulated scenarios, private or dedicated deployment is often mandatory. In that case, evaluate hardware compatibility, support for common acceleration methods, and whether clear deployment documentation and version management policies are provided.

2.4 Ecosystem and toolchain

Whether a model can be quickly integrated into a business also depends on the completeness of its surrounding toolchain. Consider:

• Support for common inference frameworks and deployment formats
• Stable structured output capabilities
• Support for tool calling and multi-step reasoning
• Ease of integration with existing gateways, monitoring, and logging systems
• Observability, such as token usage, error rates, and latency distribution

A model with an incomplete toolchain can consume significant engineering time during integration, even if the model itself is capable.

2.5 Cost structure and long-term evolution

Cost is not just unit price. Evaluate the full picture:

• Pricing model for input and output tokens
• Additional overhead in long-context scenarios
• Resource growth curve when scaling concurrency
• Migration costs caused by model version iteration
• Availability of smaller distilled versions for cost reduction in simple scenarios

A reasonable approach is to use a stronger model for core complex scenarios and a lightweight model for high-frequency simple scenarios, balancing experience and cost.

3. Using unified inference platforms to reduce experimentation cost

Recently, many teams have noticed that some inference platforms provide access to multiple newly released domestic models. The value of such platforms is not deciding which model is best, but lowering the cost of side-by-side comparison.

Traditionally, comparing two models required applying for separate access, preparing two environments, writing two sets of calling code, and handling two response formats. On a unified inference platform, differences between models are abstracted away at the API layer, and developers can switch models quickly for A/B testing with the same code.

The following example wrapper illustrates the convenience of a unified interface:

def call_model(platform_client, model_name, messages, temperature=0.7):
    response = platform_client.chat(
        model=model_name,
        messages=messages,
        temperature=temperature,
    )
    return {
        "model": model_name,
        "answer": response["content"],
        "usage": response.get("usage", {}),
        "latency_ms": response.get("latency_ms"),
    }
# Test different models with the same business logic
for model in candidate_models:
    result = call_model(client, model, test_messages)
    evaluate(result)

This approach significantly shortens the time from hearing that a model looks promising to confirming whether it fits your business. It does not depend on any specific product, only on whether the platform provides the target model and whether the API is stable enough.

One caveat: unified platforms are usually suitable for early validation and lightweight production use. For high-concurrency, strongly regulated, or deeply customized inference scenarios, you may still need to migrate to an independently deployed environment later.

4. From testing to production: a minimal practical path

Based on the framework above, here is a practical selection process suitable for small and medium-sized teams.

Step 1: Define key scenarios and success criteria

Do not rush into testing models. Identify the core parts of your business that actually depend on LLMs, and define what good means. For example:

• Customer service: answer accuracy, refusal rate, average number of turns
• Code generation: runnable rate, test pass rate
• Document parsing: field extraction accuracy, format stability

Without success criteria, test results cannot be interpreted.

Step 2: Prepare an internal evaluation set

Extract and anonymize 50 to 200 representative samples from real business data. Samples should cover both common and edge cases, and should overlap as little as possible with public training data. The value of an internal evaluation set is far higher than public leaderboards.

Step 3: Small-scale multi-model testing

Test two to three candidate models on a unified inference platform or in your own environment using the same sample set. Record answer quality, latency, token consumption, and format compliance. Run at least two rounds to verify stability.

Step 4: Comprehensive scoring and scenario routing

Summarize evaluation results in a table and score according to business priorities. Do not chase a single optimal model. Instead, consider whether two models can cover different scenarios together. For example:

• Complex reasoning scenarios: high-capability model
• High-frequency simple scenarios: lightweight model
• Data-sensitive scenarios: privately deployed model

Step 5: Canary rollout and continuous regression

After deployment, reserve a portion of traffic for canary validation and continuously collect failure samples. Run regression tests every two weeks or monthly with an updated evaluation set to check for capability degradation. A model update does not guarantee improved business outcomes; decisions must be driven by data.

5. Easily overlooked engineering issues

Beyond the model itself, the following issues frequently appear in real projects and deserve early attention.

Security and compliance: whether model outputs may leak sensitive information from training data; whether secondary output filtering is required; whether data residency and retention periods meet requirements.

Containerization and elastic scaling: whether the inference service can be easily containerized; whether it can scale quickly during traffic spikes; whether cold start time is acceptable.

Observability: whether you can obtain token usage, P99 latency, error code distribution, and similar metrics; whether integration with existing monitoring and alerting systems is straightforward.

Prompt and version management: whether prompts need retuning after a model upgrade; how to manage combinations of different model versions and prompt versions.

None of these issues appear on leaderboards, but they all affect the stability of production systems.

6. Conclusion: choose fit, not hype

When multiple domestic LLMs are released around the same time and appear on the same inference platforms, the first instinct is often to ask which one is stronger. The better question is: which one fits my scenario, my team, and my cost structure.

There is no absolutely correct model, only more suitable combinations. Build your own evaluation set, validate quickly through a unified interface, pay attention to inference performance and engineering details, and iterate through canary releases and regression testing. That is how LLMs can reliably deliver business value in production.