Practical Adaptation of Domestic AI Compute Cards for Trillion-Parameter Multimodal Models

1. Introduction: Background on Hardware Adaptation for Large Multimodal Models

In recent years, as Large Language Model (LLM) parameters have scaled from tens of billions to the trillion level, multimodal reasoning (joint image-text understanding and generation) has become a critical direction for capability enhancement. Large-scale models featuring million-level context windows, code generation, and long-horizon agent scheduling impose stringent requirements on underlying compute hardware and software stacks. Achieving rapid adaptation and stable inference for such models on domestic AI training and inference compute cards has become a focal point in the industry.

This article draws on the “Day-0” adaptation of a trillion-parameter multimodal large model on a domestic AI compute card to abstract a general methodology for adapting large models to domestic hardware. We will explore model characteristic analysis, software stack reuse, key computation path migration, sparse attention optimization, and end-to-end inference service integration.

2. Target Model Characteristics and Adaptation Challenges

2.1 Model Scale and Capabilities

The target model for this adaptation operates at a scale of approximately 3 trillion parameters and features the following core technical characteristics:

• Ultra-long Context Support: Supports a context window of up to a million tokens, posing severe challenges to memory capacity and attention computation efficiency.
• Multimodal Reasoning: Simultaneously supports joint text and image reasoning, involving cross-modal fusion computation between visual encoders and the language model.
• Reinforcement Learning Post-training: Optimized through large-scale post-training and reinforcement learning, demonstrating outstanding performance in code generation, 3D creation, financial research, knowledge QA, and long-horizon agent tasks.
• Sparse Attention Mechanism: Employs Dynamic Sparse Attention (DSA) technology to reduce long-sequence computation overhead, requiring hardware and software stack coordination to support dynamic indexing.

2.2 Core Adaptation Difficulties

1. Memory and Compute Matching: Weight loading, KV Cache management, and activation storage for a trillion-parameter model place immense pressure on memory bandwidth and capacity.
2. Hybrid Computation Paths: Text and visual reasoning involve different operator types (such as convolution, attention, and normalization), requiring unified scheduling.
3. Sparse Attention Indexing: The DSA mechanism requires runtime support for dynamic sparse indexing, meaning traditional dense computation paths cannot be directly reused.
4. Day-0 Timeline Requirements: Adaptation and inference services must be operational in the shortest possible time following model weight release, demanding rapid compatibility from the software stack.

3. Software Stack Architecture and Rapid Adaptation Mechanism

3.1 Multi-layer Software Stack Design

The software stack of domestic AI compute cards typically adopts a layered architecture covering the following key levels:

Layer	Functional Positioning	Core Capabilities
AI Framework Layer	Connects upstream model implementation	Provides programming interfaces compatible with mainstream inference frameworks
Operator Compilation Layer	Translates high-level computation to hardware instructions	Compiler optimization, auto-tuning
Device Runtime Layer	Drives hardware execution	Memory management, task scheduling, multi-card communication
Programming Model Layer	Developer interface abstraction	Unified parallel programming paradigm

This layered design allows model adaptation to proceed independently at each level with gradual integration, significantly boosting adaptation efficiency.

3.2 Key Adaptation Toolchain

The following toolchains played a central role during this adaptation process:

• Inference Service Framework Integration: Uses a mainstream community inference framework as the upstream entry point to handle standard processes like model weight loading, Tokenizer initialization, and inference pipeline construction.
• Operator Library Support: Relies on a high-performance operator library to achieve hardware mapping for basic mathematical operations (Matrix Multiplication, Softmax, LayerNorm, etc.).
• Domain-Specific Language (DSL) Compiler: Implements custom optimization and hardware adaptation for key computation paths through various compiler backends (such as Triton-style intermediate representations, tile-level scheduling languages, and multi-core cooperative compilers).

3.3 Technical Foundation for Rapid Compatible Migration

The “rapid compatible migration capability” of the software stack across the programming model, runtime interfaces, and AI framework layers is the foundation for achieving Day-0 adaptation. Its essence lies in:

1. Interface Standardization: Exposes Python APIs consistent with mainstream industry frameworks, reducing the volume of model code modifications.
2. Automatic Operator Mapping: Automatically matches hardware implementations for framework-called operators; uncovered operators are extended through the compiler.
3. Legacy Code Reuse: Model inference code requires no massive rewriting; incremental adaptation is only needed for newly added computation paths.

4. Key Computation Path Migration and Sparse Attention Optimization

4.1 Text Inference Path Migration

The core computation paths for text inference include:

1. Embedding and Positional Encoding: Maps input tokens to high-dimensional vectors, involving large-scale table lookups and addition operations.
2. Transformer Layer Iterative Computation: Each layer contains self-attention, feed-forward networks, residual connections, and layer normalization.
3. Decoding Phase KV Cache Management: Efficient management of key-value caches during autoregressive generation is required to reduce redundant computation.

The adaptation strategy involves mapping these paths individually to hardware operators and utilizing the compiler to perform block-scheduling optimization for Attention computation, ensuring efficient inference under limited memory.

4.2 Visual Inference Path Integration

The visual encoder of multimodal models typically employs Vision Transformer (ViT) or similar architectures, involving:

• Large-scale 2D convolution and Patch Embedding
• Visual self-attention computation
• Cross-modal projection layers (mapping visual features to the language model space)

During this adaptation, new visual computation paths were added to the software stack. Key operators of the visual encoder were compiled to the compute card’s execution units and jointly scheduled with the language model inference pipeline.

4.3 Sparse Attention (DSA) Index Implementation

Sparse attention is a key technology for reducing computational complexity in long-context scenarios. Its core idea is that not all token pairs require attention computation; instead, a dynamic indexing mechanism selects only crucial tokens for computation.

Adaptation challenges include:

• Dynamic Index Construction: Different input sequences produce different sparse patterns, requiring runtime dynamic index generation.
• Index and Computation Coupling: Indexing results must directly drive data movement and block partitioning for attention computation.

The solution involves implementing hardware acceleration support for sparse indexing within the device runtime layer of the software stack. This allows the DSA computation path to execute on the compute card with efficiency approaching that of dense attention. Specifically, the software stack adapted key modules including index construction, sparse matrix multiplication, and index cache management for DSA.

4.4 Hybrid Attention and Multimodal Computation Scheduling

When processing joint image-text inputs, the model must simultaneously execute text self-attention, visual self-attention, and cross-modal attention. The software stack utilizes a unified task scheduler to achieve pipeline parallelism and resource reuse for different types of attention computation on the hardware, preventing compute unit idling.

5. End-to-End Inference Service Integration

5.1 Model Loading and Initialization

The complete inference service chain begins with the efficient loading of model weights:

Model weight file -> Weight parsing and sharding -> Device memory allocation -> Multi-card weight synchronization -> Compute graph construction

For trillion-parameter models, tensor parallelism is typically adopted. Weights are sharded across multiple compute cards and synchronized via high-speed interconnects. The weight loading interface provided by the software stack must support optimizations such as large file streaming reads and zero-copy memory transmission.

5.2 Service Startup and Inference Interface

Once the inference service starts, it must provide standardized API interfaces supporting:

• Single-turn/multi-turn text dialogue
• Joint image-text input reasoning
• Streaming output
• Batching

5.3 Stability Verification and Performance Baseline

Upon completion of adaptation, systematic verification is conducted:

1. Functional Correctness: Output alignment tests against reference implementations to ensure numerical precision consistency.
2. Throughput and Latency: Measuring inference throughput (Tokens/s) and Time To First Token (TTFT) across different batch sizes.
3. Memory Footprint: Monitoring peak memory usage to verify memory management strategies in long-sequence scenarios.
4. Multi-card Scaling: Verifying linear speedup and communication overhead during multi-card parallel execution.

6. Technical Insights and Best Practices

6.1 Adaptation Methodology Summary

A general methodology for adapting large models to domestic hardware can be derived from this practice:

1. Framework First, Operators Later: Prioritize ensuring the upstream inference framework runs successfully before gradually refining operator-level optimization.
2. Incremental Migration: Reuse mature implementations for existing computation paths; focus specialized development only on new paths (like DSA and visual encoding).
3. Compiler-Driven Optimization: Leverage DSL compilers to achieve hardware-aware auto-tuning, reducing manual optimization costs.
4. End-to-End Joint Debugging: From weight loading to inference output, every stage requires end-to-end verification to prevent local optimizations from creating global bottlenecks.

6.2 Recommendations for Developers

• Track Software Stack Iterations: Software stacks for domestic AI compute platforms update frequently. Timely follow-ups can provide access to new operators and performance improvements.
• Utilize Profiling Tools: Use hardware performance analysis tools during adaptation to pinpoint compute bottlenecks and guide optimization directions.
• Understand Model Compute Graphs: Deeply analyzing the operator composition and data dependencies of the target model helps accurately identify computation paths requiring adaptation.
• Emphasize Multi-card Coordination: Trillion-parameter models almost inevitably involve multi-card deployment. Plan communication topologies and load balancing strategies in advance.

7. Conclusion

The rapid adaptation of large-scale multimodal models on domestic AI compute cards signifies the accelerating maturity of the domestic AI computing ecosystem. From the layered abstraction of the software stack to the granular migration of key computation paths, and from the hardware acceleration of sparse attention to the full integration of image-text inference chains, every stage reflects the depth and breadth of software-hardware co-design. For developers and enterprise users, mastering this adaptation methodology will lay a solid technical foundation for deploying frontier large model capabilities on domestic compute platforms.