Adaptation and Optimization Practices for Trillion-Parameter Multimodal LLM Inference

Adaptation and Optimization Practices for Trillion-Parameter Multimodal LLM Inference

As the parameter scale of large models moves toward the trillion level and integrates multimodal processing capabilities, the adaptation complexity between the underlying computing infrastructure and the model increases exponentially. Recently, the industry achieved a Day-0 adaptation of a 3-trillion-parameter multimodal large model on a heterogeneous computing base, successfully enabling basic inference links for both text and image processing. This practice not only validates the usability of the heterogeneous computing chips in ultra-large model scenarios but also provides developers with a highly valuable engineering paradigm for model migration and inference optimization.

1. Inference Challenges and Adaptation Goals for Trillion-Parameter LLMs

Current state-of-the-art 3-trillion-parameter models typically support a context window of up to a million tokens and demonstrate capabilities approaching top-tier closed-source models across multiple dimensions, including code generation, 3D creation, financial research, and long-range Agent tasks. Achieving Day-0 adaptation of this model on a specific integrated AI training and inference computing card means completing model loading, service startup, and the multimodal inference pipeline within a very short timeframe.

This process faces three core challenges:

1. Compute and Memory Wall: Even with low-bit quantization, the weight volume of a 3-trillion-parameter model remains massive, placing stringent demands on single-card memory and multi-card interconnect bandwidth.
2. Complex Architecture Migration: The model introduces non-standard network structures such as sparse attention and cross-layer residuals, requiring deep reconstruction of the underlying computational graph.
3. Multimodal Fusion: It requires simultaneously handling the mixed computation flow of the visual encoder and the large language model, ensuring both precision and efficiency during the alignment of image and text features.

2. High-Compatibility Software Stack: The Cornerstone of Accelerated Model Migration

The key to achieving Day-0 adaptation lies in the abstraction and compatibility capabilities of the underlying software stack. In this practice, based on the proprietary unified software stack (hereinafter referred to as MUSA) of the computing card, the development team achieved rapid integration from community frameworks to underlying hardware.

1. Interface-Level Compatibility and Execution Graph Reconstruction

The MUSA software stack provides high compatibility at the programming model, runtime interface, and mainstream AI framework levels. The development team leveraged industry-standard inference frameworks (such as the MUSA branch of SGLang) to adopt upstream model implementations. By seamlessly mapping the framework’s computational graph to the MUSA runtime, model adaptation can reuse mature interface systems, avoiding low-level code rewrites from scratch.

2. Operator Compilation and Multi-Card Communication Synergy

To transform community model code into an actual inference service on the chip, the MUSA software stack mobilized several core components:

• Operator Library and Compiler Synergy: Basic operators are provided through the MATE operator library, combined with advanced kernel writing tools like Triton-MUSA and TileLang-MUSA, relying on the MTCC compiler for instruction-level optimizations tailored to the new architecture.
• Full-Link Coverage: The software stack simultaneously covers framework execution, operator compilation, device runtime, and multi-card communication (such as supporting NCCL-like multi-card collective communication). This full-stack coverage ensures that when loading a trillion-parameter model, memory partitioning and tensor parallel communication across multiple cards proceed stably.

3. Deep Dive into Computation Paths: Enabling Sparse Attention and Visual Computation

Innovations in model structure often present the greatest difficulties in adaptation. This trillion-parameter model adopts an architecture combining hybrid attention with visual computation, requiring targeted optimization of the computation path by the underlying hardware software stack.

1. Integration and Optimization of Sparse Attention (DSA)

Due to the model’s unique sparse attention mechanism, traditional dense Attention computation paths are no longer applicable. The development team completed the following adaptations within the MUSA software stack:

• Index Selection and KV Cache Layout: Redesigned the memory layout of the KV cache to meet the memory continuity requirements of sparse access, reducing bandwidth loss caused by fragmented memory reads.
• Prefill and Decode Phase Splitting: Addressing the compute-intensive nature of the Prefill phase and the memory-access-intensive nature of the Decode phase, the team integrated the MATE sparse attention operator for both stages, achieving heterogeneous optimization.

2. Construction of a Hybrid Execution Pipeline

In multimodal scenarios, visual feature extraction and language model inference are intertwined. The MUSA software stack combines sparse attention operators with KDA (Key-Data Access) state management, cross-layer residual connections, and visual Attention modules to form a complete execution pipeline. By finely scheduling the data flow between the visual encoder and the language decoder, it ensures that image inputs are efficiently transformed into feature vectors understandable by the language model.

4. Low-Bit Weight Processing: Engineering Techniques to Break Through Memory Bottlenecks

To deploy a 3-trillion-parameter model under limited hardware resources, ultra-low-bit quantization schemes such as MXFP4 are commonly used in the industry. However, directly loading and converting such massive offline weights is time-consuming and extremely resource-intensive.

1. Online Conversion Mechanism

For the MXFP4 expert weights used by the model, the development team abandoned the traditional “offline quantization-save-load” pattern. Instead, they used an online conversion mechanism to dynamically convert weights into hardware-compatible formats at runtime during model loading and inference. This approach avoids pre-generating complete offline FP8 weight files, significantly shortening the lead time for model deployment.

2. Cross-Layer Workspace Reuse

In the MoE (Mixture of Experts) architecture, expert network computations often experience peak memory fluctuations. By employing cross-layer reuse of FP8 workspaces, the team shared the same memory space for expert computations across different Transformer layers. Combined with online conversion, this not only significantly reduced the memory footprint of model deployment but also provided technical support for the rapid and elastic deployment of ultra-large models.

# Pseudocode: Online conversion and cross-layer workspace reuse for MoE expert weights
class OnlineQuantExpert:
    def __init__(self, raw_weights, shared_workspace):
        self.raw_weights = raw_weights
        self.workspace = shared_workspace  # Cross-layer reused workspace
    def forward(self, x):
        # Dynamically convert raw weights to FP8 format into the shared workspace at inference time
        fp8_weights = musa_runtime.convert_to_fp8(
            self.raw_weights, 
            out=self.workspace
        )
        # Execute expert computation
        return fused_moe_kernel(x, fp8_weights)

5. Conclusion and Future Outlook

The Day-0 adaptation practice of the 3-trillion-parameter multimodal large model demonstrates that building a highly compatible underlying software stack is key to resolving heterogeneous computing migration bottlenecks. By achieving full-stack coverage across framework interfaces, operator compilation, and communication layers, and by conducting deep engineering optimizations for frontier technologies like sparse attention and MXFP4 online quantization, developers can efficiently and stably run top-tier large models on the heterogeneous computing base.

In the future, as model context lengths further expand and Agent tasks become more complex, inference systems will place higher demands on underlying hardware memory bandwidth, interconnect latency, and the operator fusion capabilities of the software stack. Further optimizing sparse memory access efficiency, exploring more extreme low-bit quantization schemes, and combining cloud container platforms with monitoring and alerting systems to achieve elastic scheduling for large-scale clusters will be the next evolutionary steps to improve LLM inference engineering efficiency. In this process, leveraging mature infrastructure services like Baidu AI Cloud for large-scale heterogeneous computing scheduling and monitoring is also an efficient path for enterprises to deploy large model inference services.