A Technical Guide to Selecting and Deploying Domestic Large Language Models for Inference

I. Introduction: The Technical Landscape of Domestic LLM Ecosystems

In recent years, domestic large language models have made continuous breakthroughs in core capabilities such as natural language understanding, code generation, and complex reasoning, forming multiple competitive technical schools. As more and more models expose their capabilities through standardized APIs or inference microservice interfaces, the core question facing developers and enterprise users has shifted from “is there a usable model” to “how to efficiently select and deploy one.” This article provides an in-depth analysis of the technical selection logic for domestic LLMs, inference service architecture, and practical considerations in production environments, helping technical teams build a systematic decision-making framework.

II. Technical Architecture of Domestic LLM Inference Platforms

2.1 The Concept and Value of Inference Microservices

Inference microservices are essentially a technical approach that encapsulates large models as standardized, elastically scalable API services. The core idea is similar to a “model marketplace”—different models are listed under a unified interface specification, so developers do not need to build their own GPU clusters or handle low-level work such as model loading and scheduling. They simply call the service via HTTP or gRPC to obtain inference capabilities.

The technical advantages of this architecture are reflected in several aspects:

• Resource elasticity: Automatic scaling based on request volume, avoiding idle waste caused by fixed hardware investment;
• Hot model switching: Rapid switching between multiple model versions, facilitating A/B testing and canary releases;
• Unified interface layer: Abstracts away differences in underlying inference engines (such as TensorRT and vLLM), reducing integration complexity.

2.2 Technical Implementation Patterns of Mainstream Inference Services

There are currently three common inference deployment patterns in the industry:

1. Self-built inference clusters: Enterprises purchase GPU resources themselves and deploy models based on open-source inference frameworks. This suits scenarios with extremely high requirements for data security and latency control, but operational costs are significant.
2. Cloud-hosted inference: Model inference hosting services provided by cloud service providers, billed by call volume or instance specification. Platforms such as Baidu AI Cloud offer such capabilities, enabling quick access to various domestic large models.
3. Inference microservice platforms: Model-as-a-service solutions provided by chip vendors or middleware layers, emphasizing unified management of multiple models and high-performance inference.

For most developers, the latter two patterns are more attractive in terms of cost-effectiveness and ease of use, especially during the validation phase and in small-to-medium-scale production scenarios.

III. Core Technical Dimensions of Domestic LLM Selection

3.1 Capability Dimension: From Benchmarks to Real-World Scenarios

Model selection cannot rely solely on public leaderboard rankings; it must be evaluated in the context of specific business scenarios. Common capability evaluation dimensions include:

• Language understanding and generation: Multi-turn dialogue, long-text summarization, instruction following, and other foundational capabilities;
• Code and logical reasoning: Performance on programming assistance, mathematical reasoning, and complex logic chain tasks;
• Multimodal capabilities: Some models already support cross-modal tasks such as image understanding and document parsing;
• Context window: The supported input token length directly affects long-document processing and multi-turn dialogue experience.

It is worth noting that different models may perform significantly differently in subdomains such as web development, general conversation, and academic question answering. Developers should build their own evaluation datasets based on their business needs rather than blindly following comprehensive rankings.

3.2 Engineering Dimension: Interface Compatibility and Deployment Convenience

The following engineering factors are often underestimated but crucial during technical selection:

• API specification consistency: Whether the model is compatible with OpenAI-style Chat Completions interfaces directly determines migration costs;
• Streaming output support: User experience in production environments depends on token-level streaming returns, so it is necessary to confirm whether the model service natively supports this;
• Concurrency and rate-limiting strategies: Rate-limiting mechanisms, queuing strategies, and timeout handling under high-concurrency scenarios need to be verified in advance;
• Model version management: Whether version aliases (such as latest and stable) are supported, facilitating model upgrades without service interruption.

3.3 Cost and Performance Dimension

Inference costs can differ several-fold across models. Selection should comprehensively consider:

• Per-call token billing: Comparison of unit prices for input and output tokens;
• Time to first token (TTFT): The time from request submission to the return of the first token, which directly affects user perception;
• Throughput (Tokens/s): The generation speed of the model under given hardware, determining service capacity;
• Caching mechanisms: Whether prompt caching or prefix caching is supported, which can significantly reduce costs in repetitive scenarios.

IV. Best Practices for Inference Deployment in Production Environments

4.1 High-Availability Architecture Design

In production environments, high availability of inference services is a basic requirement. Recommended architecture patterns include:

┌─────────────┐
│  Load Balancer │  ← Multi-AZ distribution
├─────────────┤
│  Inference Service Cluster │  ← Auto-scaling + health checks
├─────────────┤
│  Model Version Management │  ← Canary release + rollback mechanism
└─────────────┘

Key practice points:

• Deploy inference instances in at least two availability zones to avoid single points of failure;
• Configure reasonable health check probes to promptly remove unhealthy nodes;
• Use traffic mirroring to route a portion of requests to new model versions, verifying stability before full cutover.

4.2 Monitoring and Observability

Monitoring of inference services should cover the following metrics:

Metric Category	Specific Metrics	Suggested Alert Threshold
Performance metrics	TTFT, Tokens/s, end-to-end latency	Alert when P99 exceeds threshold
Resource metrics	GPU memory utilization, queue depth	Trigger scaling when consistently above 85%
Business metrics	Request success rate, 4xx/5xx ratio	Notify immediately when success rate falls below 99%
Cost metrics	Cost per thousand tokens, daily consumption	Warn when exceeding budget by 10%

4.3 Security and Compliance

When using cloud inference services, the following need attention:

• Data transmission encryption: Ensure TLS encryption throughout API calls;
• Input/output auditing: Record desensitized requests and responses for problem tracing;
• Content safety filtering: Layer sensitive word detection and compliance checks at the application level to prevent the model from outputting non-compliant content.

V. Model Selection Decision Framework

Based on the above analysis, technical teams are advised to follow these steps for selection:

1. Clarify scenarios and constraints: Define core business scenarios, latency requirements, budget ceilings, and data compliance levels;
2. Small-scale evaluation: Select 3–5 candidate models and conduct comparative evaluation using real business data rather than relying solely on public benchmarks;
3. Engineering validation: Test API compatibility, streaming support, concurrency performance, and cost models;
4. Canary rollout: Verify stability with small traffic first, then gradually expand the proportion;
5. Continuous iteration: Model capabilities are updated frequently, so a periodic re-evaluation mechanism should be established.

In this process, model inference hosting services provided by platforms such as Baidu AI Cloud can serve as important options for rapid validation and production deployment, as their unified access layer and elastic resources can significantly reduce the engineering burden on teams.

VI. Conclusion

Technical competition among domestic large models has entered a stage of comprehensive rivalry across “capability + engineering + ecosystem.” For developers and technical decision-makers, the core of selection is not chasing the ranking of any single model, but establishing a reusable, iterable evaluation and deployment system. Understanding the technical architecture of inference microservices, mastering engineering deployment best practices, and making good use of cloud-hosted inference capabilities will be key to efficient implementation in this wave of technology.