Key Technical Paths for Adapting Domestic AI Accelerators to Ultra-Large-Scale Multimodal Reasoning Models

1. Background: Adaptation Challenges of Ultra-Large-Scale Multimodal Reasoning Models

Recently, the industry has released ultra-large-scale multimodal reasoning models capable of supporting million-level context lengths. These models typically feature trillion-level parameter structures and demonstrate strong capabilities in code generation, 3D content creation, financial analysis, knowledge-based QA, and long-horizon agentic tasks. In several public benchmarks, their scores in coding and agentic tasks have approached or matched leading closed-source models.

Compared to previous text-only models, multimodal reasoning models introduce visual encoding branches, cross-modal attention, and more complex sparse computation patterns. Consequently, inference deployment is no longer just about loading weights and executing matrix multiplications. It requires simultaneously enabling two computational paths—text sequences and image features—while addressing the memory pressure and compute scheduling issues introduced by long contexts.

When targeting domestic AI training and inference accelerators, the adaptation process faces additional software ecosystem barriers. There are typically multi-layer dependencies among the upper-level training frameworks, inference services, and underlying operator libraries. Any interface mismatch at any level can cause the model to fail to run stably. Therefore, this type of adaptation must start from model structure analysis, progressively completing computational graph migration, operator alignment, and runtime verification.

2. Adaptation Goals: From Model Weights to Usable Inference Services

A complete adaptation cycle typically targets three observable milestones:

1. Model Loadable: Weight formats, tensor parallelism partitioning, and device memory layouts must correctly match the accelerator’s memory architecture.
2. Service Startable: The inference service must successfully initialize the computational graph, pre-allocate memory, establish communication groups, and enter a state ready to receive requests.
3. Basic Link Runnable: Both text and image inputs must complete forward propagation and return reasonable results, maintaining acceptable consistency with the reference implementation.

While these goals seem fundamental, they are challenging to achieve on ultra-large-scale multimodal models. On one hand, the deep layer count and massive parameter size mean that a single accelerator’s memory often cannot accommodate the entire model. On the other hand, new architectures like sparse attention and hybrid modal encoding introduce unconventional operators that traditional runtimes may not support natively.

3. Breaking Down the Adaptation Path

3.1 Using a Unified Programming Model as the Intermediate Layer

In practice, an effective approach is introducing a unified programming model and runtime interface layer. This layer interfaces with upper-level training frameworks and inference engines while encapsulating device drivers and accelerator compute capabilities. By providing programming interfaces compatible with mainstream AI frameworks, it significantly reduces the cost of model code migration.

For instance, during the inference framework selection phase, a common community-serving solution can be adopted as the upstream implementation. This solution handles logic such as model loading, request scheduling, batching, and generation control. The underlying computation is then mapped to the accelerator via unified interfaces. The advantage is that the upper-level framework does not need to perceive all hardware details; it relies solely on the memory management, stream synchronization, and operator dispatch capabilities provided by the intermediate layer.

The unified programming model can also collaborate with the device runtime to accomplish the following:

• Distributing model parameters across multiple accelerators based on tensor or pipeline parallelism strategies.
• Creating independent execution streams for different compute stages to improve compute-communication overlap.
• Managing buffer space to provide stable KV cache allocation strategies for long-context inference.

3.2 Operator Migration and Compilation Optimization

New model architectures typically introduce a set of unconventional operators. The adaptation team must leverage operator libraries, compilers, and custom kernel toolchains to migrate these operators from community code to the target accelerator.

In practice, operator migration can proceed in the following order:

1. Identify Critical Paths: Through computational graph analysis, identify the set of operators with the highest execution time consumption, prioritizing efficient execution on the accelerator.
2. Reuse Mature Interfaces: For conventional matrix multiplications, layer normalizations, and activation functions, directly reuse existing implementations to reduce redundant development.
3. Add Custom Implementations: For new structures like sparse attention, visual feature projection, and cross-modal fusion, write dedicated kernels based on device programming interfaces.
4. Compiler Fallback: For highly complex but infrequently called operators, use the compiler to generate an executable version first to ensure functional correctness, then optimize performance iteratively.

The compilation pipeline generally needs to support the conversion from high-level operator representations to low-level device code. Some teams adopt a layered approach: the frontend accepts Python-style tensor expressions or domain-specific languages; the mid-end performs loop transformations and memory optimizations; the backend generates executable code for the target device. This design allows generating runnable implementations for different accelerators without altering the upper-level model code.

3.3 Sparse Attention and Long-Context Support

Ultra-large-scale multimodal reasoning models often employ sparse attention mechanisms to reduce the computational complexity of processing long sequences. Unlike traditional dense attention, sparse attention requires dynamically selecting elements for computation based on the relevance of queries and keys before calculation. This imposes higher demands on hardware indexing, memory access patterns, and operator dispatch.

During adaptation, the following issues must be prioritized:

• Index Generation: Generate candidate indices based on sparse patterns and efficiently transfer index data to the accelerator.
• Memory Access Optimization: Avoid memory bandwidth waste caused by random access; organize sparse blocks into contiguous or semi-contiguous layouts wherever possible.
• Compute Masking: Apply masks within attention kernels to block the impact of invalid positions on the final result.
• Cache Management: Design scalable KV cache strategies for long-context scenarios to prevent memory from depleting rapidly as sequence length grows linearly.

To support million-level contexts, chunked attention or hierarchical attention schemes are typically required. The inference service can split long sequences into chunks, process them on different devices or compute stages, and then reduce the inter-chunk results. This controls memory usage per computation while preserving global semantic modeling capabilities.

3.4 Integrating Dual Visual-Text Pathways

Compared to text-only models, multimodal reasoning models add a visual encoding pathway. Image inputs must first pass through a visual feature extraction module to generate feature representations aligned with the dimensions of text hidden vectors. Subsequently, visual and text features enter cross-modal attention layers together to fuse information.

During adaptation, both pathways must be verified separately:

• Text Pathway: Verify the end-to-end availability from tokenization and embedding to generation output.
• Image Pathway: Verify the computational correctness from image preprocessing, visual encoding, and feature projection to cross-modal fusion.

In the basic inference verification phase, simple image-text paired samples can be constructed. For example, input an image containing text information along with a text prompt, and check if the model provides a reasonable answer based on the image content. If the output meets expectations semantically and factually, the basic multimodal reasoning pathway is considered successfully integrated.

4. Key Points for Inference Service Integration

After completing underlying operator and computational graph adaptation, these capabilities must be exposed as a stable inference service. Service integration typically involves the following aspects:

4.1 Request Entry and Batching

The inference service must provide a unified request entry supporting mixed text and image inputs. For multimodal tasks, image data is usually converted to tensors via a preprocessing module and then packed with text sequences into a batched request. Batching strategies must account for differences in text length and image count to avoid resource waste caused by memory fragmentation.

A common practice is dynamic batching: dynamically partitioning batches based on the distribution of sequence lengths and image sizes in the current request queue. For long-text requests, the batch size can be automatically reduced to ensure memory safety; for short-text requests, the batch size can be appropriately increased to boost throughput.

4.2 Memory Management and Cache Reuse

Long-context inference demands strict memory management. The KV cache is a primary consumer of memory. The service layer must design cache reuse mechanisms, such as memory pool-based allocators, to avoid frequent allocation and deallocation of memory blocks.

In multi-turn dialogue or long-document analysis scenarios, prefix caching can be introduced. For public prefixes shared across multiple requests, the cache is computed and stored only once. Subsequent requests directly reuse it, thereby reducing redundant computation overhead.

4.3 Communication and Multi-Card Collaboration

Ultra-large-scale models typically require multi-accelerator collaborative inference. The communication library must support efficient tensor parallelism communication primitives, such as global reductions, broadcasts, and point-to-point transfers. In multimodal models, the visual encoder and text backbone may adopt different parallelism strategies; thus, communication topology design must balance the computational characteristics of both parts.

To improve multi-accelerator efficiency, compute and communication can be executed concurrently. For example, immediately after completing the computation for part of a layer, initiate the communication operation required for the next layer. Once communication is complete, continue subsequent computation. Through stream synchronization mechanisms, the device can execute other compute tasks while waiting for communication.

5. Verification and Tuning Recommendations

Once adaptation is complete, a comprehensive verification process must be established. Basic verification focuses on functional correctness, using a small set of samples to check if outputs are reasonable. Advanced verification focuses on performance metrics, such as Time-To-First-Token (TTFT), generation throughput, peak memory usage, and multi-accelerator scaling efficiency.

During performance tuning, prioritize the following areas:

• Operator Bottleneck Identification: Use runtime profiling to find the most time-consuming operators in forward propagation.
• Memory Access Optimization: Inspect global memory access patterns of sparse attention-related kernels to reduce non-coalesced accesses.
• Parallelism Strategy Adjustment: Adjust tensor and pipeline parallelism configurations based on the compute-to-communication ratio of each model layer.
• Cache Strategy Optimization: Adjust cache pre-allocation and reuse strategies based on the actual sequence length distribution of the business.
• Compilation Optimization Level: After functional stability is ensured, incrementally increase compiler optimization levels, enabling automatic loop unrolling, vectorization, and memory layout optimization.

6. Implications for Engineering Teams

The adaptation practice of ultra-large-scale multimodal reasoning models on domestic AI accelerators demonstrates that the key to achieving rapid Day-0 support does not lie in fine-grained optimization of individual operators. Instead, it relies on building a reusable migration pipeline. A unified programming model, layered operator toolchains, a robust compiler backend, and flexible communication and memory management collectively form the foundation of adaptation work.

For teams planning to deploy large models on similar hardware in the future, it is recommended to map out the interfaces of the software stack layers in advance, clarifying the responsibilities of the framework, runtime, and device layers. This way, when encountering new model structures, there is no need to overturn existing implementations; only local adaptation for the new compute paths is required. Furthermore, progressively conducting performance tuning only after functional correctness is guaranteed effectively mitigates overall engineering risks.

As large model capabilities continue to advance, demands for multimodal reasoning, long-context processing, and multi-agent collaboration will keep evolving. For acceleration hardware, the ability to quickly support new models via a mature software stack has become a crucial metric for engineering viability. For developers, understanding the complete link from model structure to device execution will also become a critical skill for building efficient inference services.