Adapting Ultra-Large Parameter Models on Domestic GPUs: From Day-0 to Multimodal Inference

I. Background: Compute Adaptation Challenges in the Era of Ultra-Large Parameter Models

In recent years, the parameter scale of large language models has continued to climb, rapidly jumping from the tens of billions to hundreds of billions, trillions, and even tens of trillions. This exponential growth in parameter scale not only brings a qualitative leap in model capabilities but also places unprecedented demands on underlying compute infrastructure. In this context, how quickly domestic AI chips can adapt to frontier ultra-large models has become a key metric for measuring their ecosystem maturity.

“Day-0 adaptation” refers to the ability of a compute platform to complete the end-to-end setup of loading a model, starting the service, and executing basic inference pipelines immediately upon the model’s official open-source release. While this goal sounds straightforward, it actually involves a systematic engineering effort across multiple levels, including hardware compute power, software stack compatibility, memory management, operator coverage, and communication scheduling. For models with parameter scales reaching the trillions, the difficulty of adaptation increases non-linearly.

From a technical engineering perspective, this article systematically outlines the adaptation pathways for ultra-large parameter models on domestic compute platforms that integrate training and inference. It explores the core challenges and solutions, providing a reference for developers engaged in building AI infrastructure.

II. Core Technical Challenges in Ultra-Large Parameter Model Adaptation

2.1 The Memory Wall Problem

Storing the weights of a multi-trillion parameter model is a massive challenge in itself. Taking FP16 precision as an example, 3 trillion parameters require approximately 6TB of memory space, far exceeding the VRAM capacity of a single card or even a single machine. Therefore, models must employ multi-dimensional hybrid parallel strategies—such as tensor parallelism, pipeline parallelism, and expert parallelism—to shard parameters across multiple compute devices.

Achieving this on domestic compute cards requires solving the following key issues:

• Memory Management Efficiency: The loading, caching, and swap-in/swap-out strategies for sharded parameters must deeply match the hardware’s memory hierarchy.
• Cross-Card Communication Bandwidth: Collective communication operations like All-Reduce and All-Gather introduced by parallel strategies demand extremely high inter-card interconnect bandwidth.
• Memory Fragmentation Control: Memory fragmentation during long-term operation can lead to OOM (Out of Memory) errors, necessitating fine-grained memory pool management.

2.2 Operator Coverage and Graph Optimization

Ultra-large models typically adopt complex structures such as MoE (Mixture of Experts), multi-head attention mechanisms, and Rotary Position Embedding (RoPE). Domestic software stacks must ensure these operators reach a usable level in both functionality and performance:

• Operator Functional Alignment: Ensure the numerical precision of each operator aligns with reference implementations to avoid model output anomalies caused by precision deviations.
• Operator Fusion Optimization: Fuse multiple small operators into larger ones to reduce kernel launch overhead and memory access frequency.
• Computation Graph Optimization: Improve overall execution efficiency through graph-level passes such as dead code elimination, constant folding, and layout transformation.

2.3 Complexity of Multimodal Inference Pipelines

Current frontier models generally possess multimodal understanding capabilities, such as processing text and images. The multimodal inference pipeline involves the collaborative work of visual encoders, cross-modal alignment modules, and language decoders. Its adaptation difficulty is significantly higher than that of pure text models:

• Visual Encoder Adaptation: Requires support for dynamic resolution inputs, patch splitting, and position encoding operations.
• Cross-Modal Projection Layer: Ensure the mapping from visual features to the language feature space executes correctly on domestic hardware.
• Heterogeneous Compute Scheduling: Computation graphs of different modalities must be uniformly scheduled within the same inference framework, placing higher demands on the runtime system.

III. Adaptation Methodology for Training-Inference Integrated Architecture

3.1 Design Philosophy of Training-Inference Integrated Compute Cards

The core idea of a training-inference integrated architecture is that the same hardware platform can support both large-scale training tasks and efficient inference services. This design philosophy offers significant advantages for ultra-large model adaptation:

• Precision Consistency: Training and inference use the same hardware precision semantics, avoiding iterative adaptation issues caused by precision discrepancies.
• Operator Reusability: Operators validated during the training phase can be directly used for inference, reducing repeated development and debugging costs.
• Resource Elasticity: Compute resources can be dynamically allocated between training and inference based on business workloads.

3.2 Layered Adaptation Strategy for the Software Stack

At the software stack level, a layered adaptation strategy is recommended, working from the bottom up:

Layer 1: Driver and Runtime Layer
Ensure basic functions like device drivers, memory management, and stream scheduling operate normally. This layer is the prerequisite for all subsequent adaptation work.

Layer 2: Communication Library Layer
Verify the correctness and performance of collective communication operations under the target topology. For ultra-large models, communication efficiency often determines the lower bound of overall inference throughput.

Layer 3: Operator Library Layer
Verify the functional correctness of required operators one by one. It is recommended to use a unit testing framework to compare the numerical precision of each operator.

Layer 4: Framework Layer
Complete the adaptation of model loading, graph construction, and execution scheduling at the deep learning framework level. This layer requires deep integration with the framework’s model parsing logic.

Layer 5: Service Layer
Complete the engineering adaptation for inference service startup, request scheduling, and batching strategies to ensure end-to-end usability.

3.3 Engineering Practices for Day-0 Adaptation

To achieve Day-0 adaptation, thorough preparation must be completed before the model is open-sourced:

1. Obtain Model Structure Information in Advance: Analyze the model’s network structure, operator composition, and parallel strategies in advance through technical reports and papers.
2. Prepare Adaptation Test Cases: Write test cases for the model’s key operators and subgraphs in advance.
3. Build an Automated Verification Pipeline: Automate steps like model loading, inference execution, and result comparison to shorten the verification cycle.
4. Establish a Fast Rollback Mechanism: If blocking issues are encountered during adaptation, quickly roll back to a known stable version to avoid impacting overall progress.

IV. Complete Pipeline from Model Loading to Multimodal Inference

4.1 Model Loading and Weight Conversion

Weights for ultra-large models are usually released in specific formats, which may involve sharded storage and quantization compression. During adaptation, the following is required:

• Parse the model configuration file to identify parallel strategies and sharding rules.
• Load the weights into the corresponding device VRAM, ensuring the shard layout matches the computation graph.
• If precision conversion (e.g., FP8 to FP16) is involved, ensure numerical stability during the conversion process.

Below is a schematic model loading process:

# Pseudo-code example: Model loading and parallel strategy initialization
def load_large_model(config_path, checkpoint_path, device_mesh):
    config = parse_config(config_path)
    parallel_strategy = build_parallel_strategy(
        tensor_parallel=config.tp_size,
        pipeline_parallel=config.pp_size,
        expert_parallel=config.ep_size
    )
    model = build_model_graph(config, parallel_strategy)
    load_checkpoint(model, checkpoint_path, device_mesh)
    model.eval()
    return model

4.2 Service Startup and Health Check

After the model is loaded, the inference service needs to be started and a health check performed:

• Service Initialization: Allocate KV Cache, initialize the scheduler, and register inference interfaces.
• Health Check: Send probe requests to verify the model responds normally.
• Performance Warmup: Execute inference several times to trigger operator compilation and cache warmup, ensuring stable latency during formal service.

4.3 Text Inference Pipeline

Text inference is the most fundamental pipeline, with core processes including:

1. Tokenizer: Convert input text into a token sequence.
2. Embedding: Map tokens to embedding vectors.
3. Transformer Layers: Execute multi-layer attention and feed-forward network calculations.
4. LM Head: Map hidden states to vocabulary probability distributions.
5. Detokenizer: Convert the output token sequence back to text.

On domestic hardware, it is necessary to ensure that operators for each of the above steps have corresponding implementations and that performance meets service requirements.

4.4 Image Inference Pipeline

The image inference pipeline adds a visual encoding phase to the text pipeline:

1. Image Preprocessing: Resize, normalize, and patch splitting.
2. Visual Encoder: Execute calculations for ViT or similar structures.
3. Cross-Modal Projection: Map visual features to the embedding space of the language model.
4. Multimodal Fusion: Concatenate visual features with text tokens and feed them into the language model.

The adaptation difficulty of this pipeline lies in the operator coverage of the visual encoder and support for dynamic input shapes.

4.5 End-to-End Tuning Recommendations

Once the pipeline is established, tuning is recommended across the following dimensions:

• Batching Strategy: Dynamically adjust batch size based on request characteristics to balance throughput and latency.
• KV Cache Management: Use solutions like PagedAttention to improve memory utilization.
• Operator Fusion: Apply fusion optimization to hotspot operators like attention and LayerNorm.
• Communication Overlap: In pipeline parallel scenarios, overlap communication with computation to hide communication overhead.

V. Summary and Future Outlook

Adapting ultra-large parameter models on domestic GPUs is a systematic engineering effort requiring synergy across hardware, software stacks, frameworks, and services. Through the design of a training-inference integrated architecture, the execution of layered adaptation strategies, and the construction of automated verification pipelines, adaptation cycles can be effectively shortened, achieving rapid Day-0 support.

Moving forward, as model scales continue to grow and multimodal capabilities strengthen, domestic compute platforms need sustained investment in the following directions:

• Larger-Scale Parallelism Support: Supporting training and inference clusters with tens of thousands of cards.
• More Efficient Communication Mechanisms: Exploring new technologies like optoelectronic hybrid interconnects and in-network computing.
• Smarter Compilation Optimization: Utilizing AI-driven compilation technologies to automatically search for optimal operator implementations and scheduling strategies.
• More Comprehensive Ecosystem Tools: Providing a full-chain toolset for model conversion, performance profiling, and precision comparison.

The maturation of domestic AI infrastructure relies on the engineering accumulation from every model adaptation. Hopefully, the overview in this article provides valuable references for relevant developers, jointly driving the prosperity of the compute ecosystem.