Deploying Trillion-Parameter LLMs on China's Domestic AI Accelerators: Key Adaptation Practices

1. Introduction: The Shift Toward Domestic Hardware for Large-Scale Model Deployment

As large language models scale from hundreds of billions to trillions of parameters, demand for underlying compute infrastructure has surged. In recent years, China’s domestically developed AI accelerator cards—designed for both training and inference—have continued to advance in compute density, memory bandwidth, and interconnect topology, providing a new option for local inference deployment. Recently, industry teams completed Day-0 adaptation of an open-source trillion-parameter model on such cards, dramatically compressing the cycle from a model’s open-source release to a working hardware inference pipeline.

This article walks through the core technical points of adapting trillion-parameter models on these platforms—from hardware constraints and model loading to inference service startup and multimodal pipelines—and closes with actionable recommendations based on general best practices.

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2. Hardware Foundation: Architecture of AI Training-and-Inference Accelerator Cards

2.1 The Dual Challenge of Compute and Memory

At FP16 precision, the weights of a trillion-parameter model alone require roughly 20 TB of device memory. Even with INT8/INT4 quantization, memory demand remains in the multiple-terabyte range. AI accelerator cards built for both training and inference typically ship with large-capacity, high-speed memory (such as HBM-series) and rely on multi-card NVLink or proprietary interconnect protocols for high-speed communication between cards.

During adaptation, developers should focus on the following hardware metrics:

• Per-card memory capacity and bandwidth: determines how large a model shard a single card can hold;
• Inter-card interconnect bandwidth: directly affects communication efficiency for tensor parallelism and pipeline parallelism;
• Precision support of compute units (e.g., Tensor Core-equivalent modules): the degree of hardware support for mixed-precision inference such as FP16, BF16, and INT8.

2.2 Advantages of a Unified Training-Inference Design

Compared with the traditional split of “training cards + inference cards,” unified training-inference cards allow inference deployments to reuse the parallelism configurations established during training, eliminating the extra overhead of model format conversion. The same set of model weights can therefore move more smoothly between training and inference clusters.

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3. Model Loading: The Critical Path from Weight Files to Device Memory

3.1 Weight Sharding and Memory Mapping Strategies

A trillion-parameter model cannot fit into a single card’s memory at once, so model parallelism is mandatory. Common approaches include:

• Tensor parallelism: splitting each layer’s weight matrices by column or row across multiple cards, with results synchronized via AllReduce;
• Pipeline parallelism: grouping model layers and deploying different groups on different cards, using micro-batch pipelining to reduce bubbles;
• Expert parallelism: for MoE architectures, distributing different experts across compute nodes.

Implementing these strategies on domestic accelerator cards requires the underlying communication library to properly support the proprietary interconnect protocol. Verifying the communication library’s correctness and performance in multi-card scenarios is one of the core tasks of the adaptation work.

3.2 Weight Format Conversion and Loading Optimization

Open-source model releases typically ship weights in safetensors or other standard serialized formats. Loading them onto a domestic platform involves:

1. Format parsing: converting the open-source format into the platform’s native tensor representation;
2. Memory pre-allocation: planning each card’s memory layout in advance according to the sharding strategy;
3. Asynchronous loading overlapped with computation: using DMA engines to move weights asynchronously from host memory to device memory, overlapping the transfer with earlier computation to reduce time-to-first-token (TTFT).

Example pseudocode:

# Pseudocode: distributed weight shard loading across multiple devices
def load_weights_distributed(weight_path, device_map):
    for layer_name, shard_info in weight_path.items():
        target_devices = device_map[layer_name]
        for device_id, offset in target_devices:
            # Asynchronously read the corresponding shard
            shard = async_read_shard(shard_info, offset)
            # Pre-allocate device memory and transfer
            allocate_gpu_memory(device_id, shard.size)
            dma_transfer(shard, device_id)
    synchronize_all_devices()

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4. Service Startup: Connecting the Inference Engine and Runtime

4.1 Adaptation at the Inference Engine Layer

Once weights are loaded, the model must be mounted onto an inference serving framework. This step usually involves:

• Computation graph construction and optimization: translating the model’s forward-pass logic into a computation graph executable by the underlying hardware, including operator fusion and memory planning;
• KV cache management: for autoregressive generation, the memory allocation strategy for key-value caches directly affects concurrency and maximum sequence length;
• Scheduling configuration: including continuous batching, dynamic batching, and request prioritization.

4.2 The Central Role of the Domestic Software Stack

In the domestic accelerator ecosystem, the underlying software stack—which typically wraps the GPU instruction set behind a unified programming model—serves as the bridge between model and hardware. It needs to provide:

• A unified operator library: covering core Transformer operators such as Attention, LayerNorm, and MLP;
• A runtime scheduler: managing multi-card task dispatch, memory pool reuse, and asynchronous execution queues;
• Multimodal extension interfaces: if the model supports both text and image input, the inference pipeline for the vision encoder (e.g., ViT) must also be adapted.

The significance of Day-0 adaptation is that all of these links are connected within a very short time after the model’s release, allowing developers to move quickly into performance tuning rather than getting stuck on basic usability.

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5. Building the Multimodal Inference Pipeline

5.1 Technical Differences Between Text and Image

Text inference is dominated by autoregressive decoding, with generation speed and first-token latency as the key concerns; image understanding involves forward inference through a vision encoder, with throughput and resolution support as the key concerns. The two differ in compute patterns, memory usage characteristics, and batching strategies.

5.2 Unified Serving Architecture

To support both modalities within a single service instance, a typical architecture includes:

• A modality routing layer: dispatching requests to the corresponding compute pipeline based on request type;
• Shared model weight management: when the vision encoder and language model share backbone parameters, a sensible strategy for shared versus independent weight loading must be designed;
• A unified API gateway: exposing standardized interfaces externally while adapting preprocessing and postprocessing logic for each modality internally.

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6. Performance Tuning and Outlook

6.1 Key Optimization Directions

• Quantized inference: INT4/INT8 quantization can reduce memory usage by 4–8x, and combined with the low-precision compute units on domestic cards, it can significantly boost throughput;
• Speculative decoding: using a small model to quickly draft candidate tokens and the large model to verify them in parallel, improving generation efficiency;
• Memory offloading: offloading inactive KV caches or intermediate activations to host memory to support longer contexts.

6.2 Ecosystem Outlook

As the software stacks of domestic accelerator cards continue to mature and the open-source LLM community remains active, Day-0—and even Day-Negative adaptation (completing pre-release preparation before a model is open-sourced)—will become the norm. For enterprise users, choosing a domestic compute platform with a healthy software ecosystem and sustained update capability is a key decision factor for private large-model deployment.

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7. Conclusion

Rapid adaptation of trillion-parameter models on domestic AI training-and-inference accelerator cards is the result of three forces working together: hardware capability, software stack maturity, and engineering execution. From sharding strategies during model loading and scheduling optimization for inference services to unified multimodal pipelines, every step demands deep system-level understanding. The technical points and best practices outlined in this article are intended to serve as a practical reference guide for developers and enterprise technical decision-makers deploying large models on domestic platforms.