Adapting Ultra-Large Multimodal Models on Domestic Accelerators: A Technical Guide

I. Background: The Shift Towards Open Ultra-Large Multimodal Models

Recently, models with million-level context windows and trillion-scale parameters have begun releasing their full weights. These models typically undergo massive post-training and reinforcement learning, showing significant improvements in code generation, 3D content creation, financial analysis, knowledge QA, and long-horizon agent tasks. Crucially, their capabilities are no longer limited to plain text but now cover image input and joint image-text reasoning scenarios.

From an engineering perspective, this increased complexity means:

• Text and visual encoding paths must be simultaneously enabled;
• Sparse attention mechanisms introduce new indexing and computation patterns;
• Ultra-long contexts place higher demands on memory, communication, and scheduling strategies;
• Inference services must balance first-token latency, throughput, and multi-card scalability.

For domestic AI accelerators, completing the adaptation shortly after model weights are released tests not only hardware computing power but also the maturity of the software stack across programming models, operator ecosystems, compilers, and distributed communication.

II. Adaptation Goals and Technical Challenges

The adaptation goals can be divided into three levels:

1. Basic Availability: The model can successfully load weights and start the inference service.
2. Pipeline Connectivity: Both text and image inputs can complete forward inference, with outputs meeting basic expectations.
3. Engineering Reusability: The adaptation solution does not rely on one-off patches but consolidates into reusable operators, compiler optimizations, and runtime configurations.

The main technical challenges focus on four areas:

2.1 Novel Model Structure and Complex Computation Paths

The target model is not a simple stack of Transformers. It integrates visual encoding, text encoding, mixed attention, and sparse computation for long contexts. The traditional approach of “replacing a few operators to run” no longer works. A systematic review from model loading, tensor parallelism partitioning, computation graph mapping, to runtime scheduling is required.

2.2 Non-Contiguous Memory Access from Sparse Attention

The model introduces sparse attention mechanisms for long sequences. Index generation, block selection, and attention computation are interdependent. This pattern causes massive non-contiguous memory access during computation, challenging operator implementation and memory bandwidth utilization.

2.3 Unified Scheduling for Visual and Text Paths

Image-text reasoning involves more than encoding text and images separately. It requires cross-modal attention, positional encoding fusion, and batch processing strategies for varying input lengths. The unified scheduling layer must perceive the computational characteristics of different modalities to prevent a sudden drop in device utilization caused by a specific input type.

2.4 Coexistence of Multi-Card Communication and Ultra-Long Contexts

Million-level contexts mean a significant increase in KV cache size. A single card’s memory often cannot hold it entirely, necessitating cross-card partitioning. Partitioning strategies affect not only memory footprint but also communication patterns. The frequency and granularity of collective communication operations like All-Gather and Reduce-Scatter need re-evaluation.

III. Software Stack Perspective: From Programming Model to Operator Compilation

The ability to complete the adaptation in a short timeframe relies on the underlying software stack providing relatively complete migration interfaces. A standard accelerator software stack should include at least:

• Programming model and runtime interfaces;
• Device memory management;
• Collective communication libraries;
• Operator libraries and templates;
• Compilers and code generation tools;
• Upper-layer framework adaptation layers.

In this practice, the team confirmed the model code was implemented based on a mainstream inference framework. This framework receives upstream model definitions through a unified entry point and dispatches the computation graph to the device layer. The software stack must complete key path mapping during this process.

3.1 Framework Execution Layer Adaptation

The framework layer handles graph partitioning, operator scheduling, and memory lifecycle management. For new models, focus on:

• Graph recompilation caused by dynamic shapes;
• Control flow and data flow mixing caused by sparse indexing;
• Branch merging strategies for multimodal inputs.

An effective strategy is to “run the default path first, then optimize branches gradually.” Initially bypass some high-performance optimizations to ensure both text and image paths complete end-to-end inference, then replace hot operators.

3.2 Operator Library and Template Reusability

Standard operators in the model, such as layer normalization, linear layers, activation functions, and rotary positional encoding, can directly reuse existing operator libraries. However, new architectures often introduce non-standard computation patterns:

• Concatenating and chunking Key/Value from different sources in mixed attention;
• Index construction and block selection in sparse attention;
• 2D positional encoding and feature splitting in visual encoders.

For these computations, development costs can be reduced using templated operators and code generation tools. Template operators allow developers to describe the computation logic, while the underlying compiler generates device-specific instructions.

3.3 Compiler Collaborative Optimization

The compiler plays a pivotal role. It translates high-level operator descriptions into executable device code and automatically optimizes memory access patterns and computation intensity. For sparse computation scenarios, the compiler can:

• Identify regularities in index generation to reduce redundant branches;
• Improve data locality through loop reordering;
• Separate scalar and vector computations to improve instruction issuance efficiency;
• Generate dedicated memory access paths for block-sparse structures.

These optimizations generally do not require writing extensive assembly but demand strong graph analysis and code generation capabilities from the compiler.

IV. Key Path Breakdown and Implementation

4.1 Model Loading and Initialization

Loading weights for ultra-large models requires balancing memory and speed. Common practices include:

• Weight shard loading: distributing weights directly to corresponding devices along tensor parallelism dimensions;
• Using memory mapping to reduce host-side memory peaks;
• Lazy loading for certain weights, reading from storage only during the first computation.

During initialization, parameter validation of the model structure is necessary. For new models, load a small-scale configuration for a smoke test to ensure the computation graph builds completely before switching to full weights.

4.2 Text Path Connectivity

The core of the text path is Tokenization, Embedding, multi-layer Transformer stacking, and output sampling. During adaptation, prioritize running a single-card forward pass before expanding to multi-card tensor parallelism.

In long-context scenarios, mind the precision and length extrapolation capabilities of positional encoding. Dynamic shapes may cause computation graph inconsistencies between batches, requiring the framework to support efficient graph caching and recompilation strategies.

4.3 Image Path Connectivity

The image path generally includes image preprocessing, visual encoder, feature projection, and fusion with text features. The visual encoder is computationally intensive and sensitive to memory bandwidth. Adaptation considerations include:

• Splitting images into fixed-size patches for batch encoding;
• Applying dimensionality reduction projection to visual features to reduce subsequent attention computation;
• Scheduling the visual encoder as an independent stage in multi-card scenarios to avoid frequent switching with the text stage.

During image-text fusion, explicitly define attention mask construction rules. Text tokens can only attend to themselves and preceding text, while image tokens typically allow bidirectional attention. Mask generation logic must match the model configuration to prevent output quality anomalies.

4.4 Sparse Attention Integration

Sparse attention consists of three sub-phases: index generation, block selection, and attention computation. Index generation has low computation but complex logic, easily becoming a synchronization bottleneck between the CPU and the device.

It is recommended to keep index generation on the device to reduce host-device data copies. Attention computation after block selection can reuse standard attention operators but requires support for non-contiguous Key/Value blocks. For block-sparse scenarios, generate dedicated kernels based on templates, taking block indices as input to complete dynamic memory access on the device.

V. Service Deployment and Inference Validation

Once adaptation is complete, package the model as an externally accessible inference endpoint. Use a standardized inference service framework supporting:

• Weight pre-loading and persistent video memory;
• Request queuing and dynamic batching;
• Unified interfaces for text and image inputs;
• Streaming output and timeout control.

During service startup, confirm model loading via a health check endpoint, then send standard samples for validation. Text samples should cover short inputs, long inputs, and instruction-following; image samples should cover single-image QA and multi-image comparison.

During validation, besides checking output reasonableness, record key metrics:

• First-token latency;
• Single-token generation time;
• Per-request memory peak;
• Throughput under concurrent requests;
• Memory growth curve for long-context scenarios.

This data provides a baseline for subsequent parameter tuning.

VI. Performance Optimization Recommendations

After functional connectivity, optimize further from the following aspects:

Operator Fusion: Fuse adjacent linear transformations, normalizations, and activation functions to reduce kernel launch overhead.
Memory Pooling: Use pre-allocated memory pools for KV caches and intermediate activations to reduce runtime allocation and deallocation.
Communication Optimization: Overlap communication operations with computations based on tensor and sequence parallelism strategies to reduce idle time.
Batching Strategy: Group requests with similar lengths to lower padding ratios and increase the proportion of effective computation.
Low-Precision Inference: Apply low-precision quantization to certain linear layers where precision loss is tolerable to improve throughput and reduce memory footprint.

Maintain continuous observation during optimization to avoid introducing complex strategies too early, which complicates debugging. Follow the “stable first, optimize second, expand third” sequence.

VII. Adaptation Methodology Summary

This practice can be abstracted into an adaptation workflow for large models on heterogeneous accelerators:

1. Structure Analysis: Identify standard modules and new computation paths in the model to assess operator gaps.
2. Layered Migration: Framework execution first, then operator implementation, finally compiler optimization to avoid reworking from the bottom up.
3. Minimum Link Verification: Run loading, forward pass, and output with minimal input to expose structural issues early.
4. Modality Divide and Conquer: Verify text and image paths separately before combining for joint inference.
5. Performance Baseline: Record key performance metrics after functionality stabilizes as a basis for optimization.
6. Capability Consolidation: Integrate new operators, compiler optimizations, and configuration templates into the unified software stack as reusable assets.

For software stacks with robust programming models and compiler capabilities, adaptation shifts from “writing massive low-level kernels manually” to “template description - compiler generation - runtime scheduling,” shortening the cycle from model release to availability.

As ultra-large models continue evolving towards multimodality, ultra-long contexts, and agentic capabilities, challenges for underlying accelerators and software stacks will grow. Rapidly completing basic adaptation upon model release and continuously consolidating engineering capabilities will become critical standards for measuring the maturity of heterogeneous computing platforms.