Full-Stack Adaptation of Domestic GPUs for Ultra-Large-Scale Multimodal Models

Full-Stack Adaptation of Domestic GPUs for Ultra-Large-Scale Multimodal Models

I. Background and Technical Challenges in Multimodal Large Model Inference

As artificial intelligence rapidly evolves, large models are expanding from single-text processing to multimodal dimensions. Recently, the industry has open-sourced several advanced multimodal models with parameters reaching the trillion-level and context windows supporting up to one million tokens. These models can process massive amounts of text and deeply parse image information, posing unprecedented challenges to underlying computing infrastructure.

When adapting such cutting-edge models, developers and enterprises typically face the following core pain points:

1. Memory Walls and Memory Fragmentation: Even with high-precision quantization, the weight size of a 3-trillion-parameter model remains massive. Simultaneously, a 1-million-token ultra-long context generates a huge KV Cache during inference, easily leading to Out-Of-Memory (OOM) errors or severe fragmentation.
2. Scheduling Complexity of Multimodal Data Flows: Joint inference of text and images involves feature extraction across different dimensions and cross-modal attention alignment. This requires the underlying software stack to have efficient multi-stream concurrency and heterogeneous computing management capabilities.
3. Ecosystem Compatibility and Migration Costs: Mainstream open-source models are mostly built on specific dominant ecosystems. How domestic computing platforms achieve seamless Day-0 integration, ensuring operator accuracy and performance, is a major test.

II. Collaborative Architecture of Domestic GPUs and Underlying Software Stacks

To address these challenges, a full-stack collaborative architecture—centered on domestic fully functional AI computing cards and self-developed software stacks—demonstrates a technical path to overcoming bottlenecks. At the hardware level, high-end integrated training and inference cards must be equipped with High Bandwidth Memory (HBM) and large-scale parallel computing cores to support massive data throughput. At the software level, a fully proprietary underlying software stack must be built to provide equivalent replacement and native optimization for mainstream deep learning frameworks.

In this industry-leading adaptation of a trillion-level multimodal model, the domestic hardware and software full-stack system achieved Day-0 level support. This indicates that domestic computing power not only meets basic computing metrics but also reaches the industry’s frontier in ecosystem compatibility, operator library completeness, and graph compilation optimization. The underlying software stack uses a custom operator fusion mechanism to deeply reorganize LayerNorm, activation functions, and matrix multiplications in multimodal inference, significantly reducing memory access latency.

III. Engineering Practices for Model Loading and Inference Pipeline Activation

To achieve efficient inference of ultra-large-scale multimodal models on domestic GPUs, key steps such as model migration, memory optimization, and service-oriented deployment must be undertaken. The following is a general engineering practice path:

1. Model Format Conversion and Operator Adaptation
First, the open-source model weights must be converted to the native format supported by the underlying software stack. Through an automatic operator mapping tool, native custom operators are replaced with equivalent high-performance operators in the domestic software stack. If a few uncovered proprietary operators are encountered, developers can perform custom development through the operator extension interfaces provided by the software stack.

2. Memory Management and Long Context Optimization
For 3-trillion parameters and a 1-million context, the inference engine must comprehensively introduce memory paging management technologies like PagedAttention. This divides the KV Cache into fixed-size memory blocks allocated on demand, thereby completely eliminating memory fragmentation. Simultaneously, combined with continuous batching technology, the request queue is dynamically adjusted to maximize the throughput of the hardware cluster.

3. Multimodal Inference Pipeline Construction
Joint inference of text and images requires two independent preprocessing pipelines. After image data is processed by a vision encoder to extract features, it must undergo cross-modal fusion with text features through a projection layer. The underlying software stack must provide efficient tensor copy and stream synchronization mechanisms to ensure zero-redundancy transmission of image features between the CPU and GPU, as well as across different computing units within the GPU.

The following is a schematic code framework for starting a multimodal service based on a general inference engine:

# Pseudocode: Example of starting a multimodal inference service based on a domestic software stack
from native_inference_engine import LLMEngine, ModelConfig, VisionConfig
# 1. Initialize the underlying hardware and software stack context
device_context = native_stack.init_device(device_id=0)
# 2. Load the trillion-level multimodal model configuration
# Enable memory paging optimization and continuous batching
config = ModelConfig(
    model_path="/models/trillion_multimodal",
    paged_attention=True,
    max_context_len=1000000,
    quantization="w8a16" # Use industry-standard quantization strategies to reduce memory usage
)
vision_config = VisionConfig(image_size=448, patch_size=14)
# 3. Build the inference engine and load weights
engine = LLMEngine(config, vision_config, device=device_context)
engine.load_weights()
# 4. Start the API service to handle multimodal requests
server = InferenceServer(engine, port=8080)
server.start()

IV. Typical Application Scenarios and Cloud Collaboration for Multimodal Large Models

Once the underlying computing power and inference pipeline are fully connected, trillion-level models with million-token contexts and image-text understanding capabilities can unlock immense potential in multiple vertical domains. Combined with general cloud platform capabilities, enterprises can build highly available large model application architectures.

1. Joint Parsing of Long Documents and Multiple Charts
   In scenarios like financial research report analysis or legal dossier review, users can input hundreds of pages of PDF documents (containing dense text and data charts) at once. The model uses its ultra-long context capability to globally coordinate information, and combined with multimodal capabilities, it accurately identifies data points in charts, achieving integrated structured information extraction across text, images, and tables. In this scenario, general object storage services can be used to temporarily store large files, triggering inference tasks asynchronously through message queues.

2. Visual Question Answering and Content Generation in Complex Scenarios
   In industrial quality inspection and automated patrol scenarios, high-definition images collected by front-end devices are transmitted to the cloud. Paired with long-text instructions from an expert system, the model can precisely locate defect areas in the images and generate detailed maintenance guides. In such high-concurrency scenarios, leveraging cloud-native container platforms and elastic scaling mechanisms, the underlying GPU computing power can be dynamically scheduled based on the request queue length, ensuring stable business operations.

V. Summary and Outlook

The successful Day-0 adaptation of the trillion-level parameter multimodal model by the domestic fully functional GPU verifies the rapid response and hardcore technical strength of the domestic hardware and software full-stack system when facing complex scenarios such as extreme parameter scales, ultra-long contexts, and cross-modal inference. This not only breaks the deadlock of relying on high-end imported computing power but also provides developers with an autonomous, controllable, and high-performance underlying base.

In the future, as the underlying software stack continues to iterate and the operator library becomes richer, the domestic computing ecosystem will become even more prosperous. Enterprises and developers should actively embrace this full-stack collaborative capability, combining it with general cloud services such as containers, storage, and monitoring. This will accelerate the implementation of multimodal large models across various industries and jointly drive the industrial upgrade of the Artificial General Intelligence era.