Adapting Domestic Accelerator Cards for Large-Scale Multimodal Inference Models: A Technical Practice

1. Background: Large-Scale Multimodal Models Demand New Inference Infrastructure

Open-weight models are rapidly approaching the capabilities of closed-source frontier systems, particularly in code generation, long-horizon task planning, financial analysis, and 3D content understanding. Alongside this progress, parameter counts continue to grow—some models now reach trillions of parameters and support context windows of up to one million tokens. These are no longer mere text models; they are heterogeneous systems that combine visual encoding, sparse attention, mixture-of-experts structures, and complex positional encodings.

For inference infrastructure, the challenge is not simply whether VRAM can hold the weights. More importantly, the model’s internal forward computation graph has become highly differentiated: text and image pathways execute alternately, sparse attention requires dynamic indexing and online selection, long contexts demand efficient key-value cache management, and multi-expert structures impose higher requirements on communication and operator scheduling. If the underlying accelerator cards and software stack cannot quickly accommodate these new structures, even ample raw compute will not translate into a usable inference service.

Consequently, an important practice has emerged in the industry: completing the full pipeline—from model loading and service startup to basic inference connectivity—within the shortest possible time after model weights are released. This “Day-0” adaptation capability is increasingly becoming a key indicator of an accelerator card software ecosystem’s maturity.

2. Architectural Features and Computational Challenges of the Target Model

Taking a recently released large-scale multimodal inference model as an example, its core characteristics can be understood at four levels.

2.1 Parameter Scale and Context Capability

This model is the first official release in its series, with a total parameter count of 3 trillion and support for a context length of 1 million tokens. At this scale, inference typically requires multi-card collaboration—even high-capacity accelerator cards cannot hold all weights and activation states on a single device. Multi-card partitioning, pipeline parallelism, and expert parallelism must therefore be incorporated into the adaptation plan.

2.2 Interleaved Image-Text Input Path

Unlike pure text models, this model allows images and text to be interleaved at the input. The visual portion passes through an independent encoding network before fusing with text representations into the backbone network. Adaptation must connect four pipelines: image preprocessing, visual encoding, cross-modal fusion, and text decoding. A break in any one pipeline will cause the entire inference to fail.

2.3 Sparse Attention and Dynamic Structures

The model introduces a sparse attention mechanism designed for long contexts. Rather than discarding attention heads or processing segments in a fixed pattern, it dynamically selects based on query-key similarity. Implementing this structure requires low-level support for index construction, online sorting, segmented computation, and result merging—placing new demands on operator libraries and compilers.

2.4 Operator Diversity from Large-Scale Post-Training

Because the model has undergone large-scale reinforcement learning and post-training, its internal computation paths may contain operator combinations not fully covered by standard deep learning frameworks—for example, certain normalization methods, variants of rotary positional encoding, and custom activation functions under mixed precision. Adaptation work must rely on extensible operator development and compilation toolchains rather than solely on pre-built operator libraries.

3. Adaptation Approach: From Software Stack Capabilities to End-to-End Inference Service

Facing these challenges, a practical engineering approach is to start from the existing capabilities of the underlying software stack—first identifying reusable interfaces, then making targeted extensions for new computation paths. The process can be divided into five key layers.

3.1 Programming Model and Runtime Compatibility Layer

Accelerator card software stacks typically provide unified programming models and runtime interfaces to abstract hardware implementation differences. When adapting a new model, the first step is to confirm whether these interfaces cover the memory allocation, stream management, device synchronization, and data movement semantics required by the model. For most Transformer-based models, these basic capabilities already exist, avoiding the need to start adaptation at the driver level.

3.2 Framework Execution Layer Integration

Inference service startup typically depends on an upper-level inference framework. The framework layer must handle model graph loading, operator dispatch, memory planning, and execution scheduling. Adaptation can leverage the framework’s extension mechanisms for custom devices to map operator calls in the model to corresponding accelerator card implementations. The key here is maintaining compatibility with upstream model code and avoiding large-scale rewriting of model structures.

3.3 Operator Implementation and Compilation Optimization

For new operators or computation patterns appearing in the model, implementation and optimization must be completed through operator libraries or compilers. The general flow includes: identifying operator semantics, confirming input/output shapes and data types, writing device-side implementations, integrating auto-tuning mechanisms, and validating numerical correctness and performance. For sparse computations involving dynamic indexing, additional design of efficient index construction and access methods is required.

3.4 Multi-Card Communication and Parallelism Strategies

Given the model’s enormous scale, multi-card communication is critical to adaptation success. Appropriate parallelism strategies must be selected based on model structure—for example, layer-wise partitioning, head-wise partitioning, or expert-wise partitioning. For communication primitives, priority should be given to collective communication interfaces already supported by the software stack, with separate optimization for large-message transfers and fine-grained synchronization. For sparse expert models, communication jitter caused by imbalanced expert loads must also be considered.

3.5 Service Encapsulation and Validation

After completing the above steps, the model must be encapsulated as an interface capable of serving external inference requests, with end-to-end validation cases designed. Validation should cover three scenarios—pure text input, pure image input, and interleaved image-text input—checking whether output format, latency, and throughput meet basic requirements. Additionally, VRAM usage changes under different input lengths should be recorded to inform subsequent capacity planning.

4. Key Engineering Details: Integrating Sparse Attention and Hybrid Attention

In this adaptation practice, the most noteworthy discussion concerns the integration of sparse attention and visual computation.

Traditional attention computation typically performs matrix multiplication over the full set of key-value pairs, with computational complexity growing quadratically with sequence length. Sparse attention reduces computation in long-context scenarios by first building an approximate index of queries and keys, then computing exact attention only over a candidate subset. Its execution flow is roughly as follows:

1. Perform dimensionality-reduced projections on query and key vectors;
2. Build a nearest-neighbor index based on the projection results;
3. Collect candidate key-value pairs according to the index;
4. Execute exact attention computation over the candidate subset;
5. Merge results and restore the original sequence order.

This flow contains multiple dynamic-shape operators, such as index construction, segmented gathering, and sort-merge. Traditional static graph optimization methods struggle to handle such computation patterns directly. During adaptation, compiler support for dynamic shapes can be leveraged to keep index-related computation as in-graph dynamic branches while still mapping the attention core computation to highly optimized matrix operation units.

On the visual path, image inputs first pass through a visual encoder to produce feature maps, which are then aligned with text representations through cross-modal projection. The visual encoding portion typically contains many convolution or local attention operations, with computational characteristics distinctly different from the text backbone. Adaptation must ensure that accelerator cards support both computation patterns well and avoid frequent data movement between devices.

5. Software Stack Reuse and Rapid Migration Capability

From an engineering efficiency perspective, the ability to quickly adapt new models depends heavily on whether the software stack provides mechanisms for “reusing existing capabilities.”

Mature software stacks typically offer extensible interfaces at multiple layers. For example, the framework layer allows registration of custom devices and custom operators; the compilation layer supports integrating new computation patterns through intermediate representations; the runtime layer provides unified memory and stream management interfaces; and the communication layer encapsulates commonly used collective operations. When a new model introduces new structures, teams do not need to build from scratch but can make incremental adaptations on top of existing interfaces.

In this adaptation, most standard Transformer components could directly reuse existing implementations—including embedding layers, feed-forward networks, layer normalization, and conventional attention. The focus was on sparse attention index construction, visual encoding fusion, and certain special computations introduced during post-training. This “reuse most, extend a little” pattern significantly shortened the time from model release to inference service connectivity.

6. Performance Evaluation and Future Optimization Directions

Once the basic pipeline is connected, performance optimization becomes the core work of the next phase. It can proceed in the following directions.

6.1 VRAM Usage Optimization

For million-token context scenarios, the key-value cache becomes the primary VRAM consumer. Strategies such as grouped-query attention, quantized caching, sliding windows, and offloading can reduce VRAM pressure. Appropriate strategies should be selected based on actual input distributions to avoid introducing unnecessary overhead in short-input scenarios.

6.2 Operator Fusion and Compilation Optimization

Compiler-based fusion of adjacent operators can reduce VRAM read/write traffic for intermediate results. For example, scaling, masking, and Softmax in attention can be fused into a single kernel, or residual connections and layer normalization can be combined. For dynamic-shape operators, more efficient index construction algorithms and parallelization strategies should be explored.

6.3 Multi-Card Parallelism Efficiency Improvement

In multi-card scenarios, the degree of overlap between communication and computation directly affects overall throughput. Communication overhead can be further reduced by finely partitioning computational tasks, prefetching communication data, and using asynchronous collective communication. Meanwhile, dynamic load-balancing mechanisms can address imbalanced expert loads.

6.4 Service Stability and Elastic Scaling

When the model is deployed in production, attention must also be paid to long-running stability, abnormal input handling, concurrent scheduling, and elastic scaling. It is recommended to add request queuing, timeout control, and health check mechanisms at the service layer, along with monitoring and alerting systems to detect potential issues promptly.

7. Conclusion

Adapting large-scale multimodal inference models is no longer merely a matter of filling in missing operators or expanding VRAM. It is a comprehensive test of an accelerator card software stack’s capabilities across programming models, framework integration, operator compilation, multi-card communication, and service deployment.

By properly leveraging the software stack’s existing interfaces and extensibility mechanisms, the time from new model release to usability can be significantly shortened. For development teams, the key is to establish an adaptation process of “identify reusable capabilities—locate new computation paths—integrate and validate layer by layer,” while continuously focusing on performance optimization and service stability afterward.

It is foreseeable that as more open models with long-context and multimodal capabilities emerge, the rapid adaptation capability of underlying inference infrastructure will become increasingly important. Accelerator platforms with robust software ecosystems and extensible toolchains will gain more engineering practice opportunities in this round of model capability diffusion.