Adapting Trillion-Parameter Models for Domestic AI Hardware: Engineering Practices

I. Background: Deployment Challenges of Ultra-Large Parameter Models

In recent years, the parameter counts of large language models have continued to climb, with the industry shifting from hundreds of billions to the trillion-parameter scale. Model capabilities have also expanded from pure text generation to complex scenarios such as image understanding and long-horizon agentic tasks. This trend places unprecedented demands on the underlying computing platforms. Any deficiency in memory capacity, interconnect bandwidth, operator coverage, or compiler toolchain maturity can prevent the model from running stably on the target hardware.

Recently, an open-source community released the complete weights for its next-generation trillion-parameter model. Supporting a context length of up to one million tokens and undergoing large-scale post-training and reinforcement learning, the model shows significant improvements in code generation, 3D content creation, financial research, knowledge QA, and long-horizon agentic tasks. On public agent evaluation leaderboards, it ranks highly among open-weight categories, excelling in work, coding, and chat subsets. In benchmarks for terminal operations and long-horizon coding tasks, its scores closely approach those of top-tier closed-source models.

For computing platforms, the adaptation difficulty lies not merely in the sheer parameter size. The model’s architecture introduces several new computational paths, including sparse attention, Mixture of Experts (MoE) structures, low-precision expert weights, cross-layer residuals, and visual attention. If any of these paths are not correctly mapped to the target hardware, the inference pipeline will break. Consequently, “Day-0 adaptation”—achieving weight loading, service initialization, and a functional inference pipeline on the day the model weights are released—has become a critical metric for evaluating the maturity of a computing ecosystem.

II. Objectives and Overall Strategy

The adaptation objectives can be broken down into three levels:

1. Model Loadability: Complete weight format parsing, memory layout planning, and shard loading to ensure that trillion-level parameters can reside in limited memory or be swapped in on demand.
2. Service Startup: Establish the initialization process for the inference service, including operator registration, graph capture, parallel strategy configuration, and multi-card communication initialization.
3. Inferability: Execute end-to-end inference for both text and image inputs, covering the computational paths for both the prefill and decode phases.

The overall strategy is to “reuse mature interfaces and focus on new paths.” Specifically, the inference framework layer accommodates upstream model implementations, maintaining consistency with the community codebase structure. The operator layer utilizes a self-developed operator library and compiler to migrate the key computational paths of the new architecture. The runtime layer manages device scheduling and multi-card communication. This layered approach means adaptation does not require rewriting the model from scratch; instead, engineering efforts can focus strictly on the newly introduced computational patterns.

Model Implementation (Upstream Community Code)
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Inference Framework Layer (Scheduling / Parallelism / Graph Capture)
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Operator Layer (Dense Operators + Sparse Operators + Visual Operators)
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Compiler and Runtime Layer (Compiler / Runtime APIs / Multi-card Communication)
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AI Accelerator Hardware

III. Software Stack Compatibility: The Foundation of Rapid Migration

A prerequisite for rapid model adaptation is a software stack that maintains strong compatibility across three dimensions: programming models, runtime APIs, and mainstream AI frameworks. At the programming model level, support for mainstream tensor programming paradigms is necessary so community model code can run with minimal modification. At the runtime API level, device management, stream management, and memory management interfaces must align with standard industry practices to reduce semantic conversion overhead. At the framework level, the software stack must integrate with the extension mechanisms of mainstream inference frameworks, allowing model implementations to be plugged in seamlessly.

In this adaptation, the team utilized the device backend extension of an open-source inference framework to accommodate the upstream model implementation. By using a self-developed operator library, a tensor-oriented compilation frontend, and a proprietary compiler, they successfully migrated and integrated the new architecture’s critical computational paths, ultimately converting the community model code into a functional inference service on the target accelerator. The software stack covers framework execution, operator compilation, device runtime, and multi-card communication. This allowed the team to reuse mature interfaces and optimize only the newly added computational paths.

The engineering value of this strategy is clear: a large number of standard operators in the model architecture (such as matrix multiplication, normalization, and activation functions) can directly reuse existing implementations. The team only needed to focus on a few new paths, such as sparse attention and low-precision expert computation, enabling them to establish an end-to-end pipeline on the model’s release day.

IV. Implementing Sparse Attention

4.1 Computational Characteristics of Sparse Attention

In long-context scenarios, the computational overhead and Key-Value (KV) cache footprint of standard full attention mechanisms grow quadratically with sequence length, making it difficult to support a context of one million tokens. Sparse attention significantly reduces this complexity by selecting a small number of relevant key-value pairs for each query position. Its implementation typically involves three key components:

• Index Selection: Based on the similarity between the query vector and the index keys, selecting the set of key-value positions to participate in the computation for each query.
• KV Cache Layout: Under sparse access patterns, the cache is no longer organized as a continuous sequence, requiring a layout designed for non-contiguous access.
• Separation of Prefill and Decode Paths: The prefill phase involves a large number of queries and complex selection patterns, while the decode phase only adds a single query per step. The operator implementations for both differ significantly.

4.2 Adaptation Essentials

Addressing these characteristics, the adaptation team successfully mapped the index selection operator, KV cache layout planning, and the computational paths for both prefill and decode, integrating them with a self-developed sparse attention operator. Furthermore, sparse attention is not an isolated module; it must work in tandem with state management mechanisms, cross-layer residual connections, and visual attention to form a complete execution chain. In practice, the team integrated the sparse attention operator and other modules into the same execution graph to ensure data layout consistency at module boundaries, avoiding additional data movement overhead from layout conversions.

A subtle but critical detail is the numerical stability of index selection. Under low-precision computation, similarity sorting may introduce minor deviations, affecting the determinism of the selection results. Engineering practices typically mitigate this by normalizing index scores and maintaining higher precision during critical comparison phases.

V. Online Conversion of Low-Precision Expert Weights

This model stores expert weights in a low-precision format to compress the storage overhead of trillion-level parameters. Loading and computing directly in the original format may not yield optimal throughput on certain hardware. However, pre-generating a complete offline high-precision weight copy introduces additional storage and conversion time costs, conflicting with the “Day-0 adaptation” timeline.

The adaptation solution employs a combination of online conversion and workspace reuse: along the expert computation path, low-precision weights are converted on demand into a high-precision format suitable for hardware execution. The converted results are stored in a cross-layer reusable workspace for subsequent layers, eliminating the need to allocate separate conversion buffers for each layer or to pre-generate complete offline weights. This approach strikes a favorable balance between memory footprint and conversion overhead, providing a feasible path for the rapid deployment of ultra-large models.

Expert Weights (Low-Precision Storage)
        │  On-demand Conversion
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Shared Workspace (High-Precision Format, Cross-layer Reuse)
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Expert Computation (Matrix Multiplication + Routing Aggregation)

It is worth noting that the granularity of online conversion directly impacts performance. Too fine a granularity leads to frequent conversion calls and increased pipeline bubbles, while too coarse a granularity spikes peak memory usage. In practice, batch conversions are typically performed per expert group, paired with asynchronous execution to hide conversion latency.

VI. Multi-Card Parallelism and Communication Coordination

Trillion-parameter models cannot reside entirely within a single card’s memory, making multi-card parallelism essential. Common parallel dimensions include tensor parallelism, expert parallelism, and pipeline parallelism, each with distinct communication requirements. Tensor parallelism requires high-frequency all-reduce communication; expert parallelism relies on all-to-all communication for token routing; and pipeline parallelism requires point-to-point transmission of activations.

In this adaptation, multi-card communication capabilities were integrated into the software stack’s unified management, coordinated with operator execution and compilation workflows. Engineering practices reveal that the degree of overlap between communication and computation is the primary factor determining end-to-end throughput. Pipelining all-to-all communication with expert computation, and splitting all-reduce communications to hide them behind matrix multiplications, can significantly reduce communication wait times. Additionally, communication buffers and computation workspaces require unified memory layout planning to prevent memory fragmentation from blocking the allocation of large communication buffers.

VII. Validation and Future Optimization Directions

Following adaptation, validation proceeded in three phases: load, startup, and inference. First, verifying that weights could be fully parsed and correctly loaded as shards. Second, ensuring the inference service initialized correctly, with proper parallel group construction and communication domain configuration. Finally, validating end-to-end outputs using both text and image inputs, with a focus on output consistency in long-context scenarios and the reproducibility of sparse attention selection results.

Future optimizations will primarily focus on the following areas:

• Operator Fusion: Fusing index selection, mask construction, and attention computation in sparse attention into fewer operator calls to reduce intermediate data movement.
• Low-Precision Computation Path Expansion: Expanding the applicability of low-precision formats in matrix multiplication and expert computation while ensuring numerical stability.
• Scheduling Strategy Tuning: Configuring distinct batching strategies and parallel granularities for the differing workload characteristics of the prefill and decode phases.
• Long-Context Memory Management: Optimizing the swap-in/swap-out strategy for the KV cache to ensure stable service under one-million-token contexts.

VIII. Conclusion

Achieving Day-0 adaptation for trillion-parameter models on domestic AI hardware is fundamentally a cross-layer systems engineering effort. The framework layer ensures low-cost migration of model code; the operator layer bridges new paths like sparse attention and visual computation; the compiler and runtime layers guarantee device execution and multi-card communication efficiency; and the online conversion strategy for low-precision expert weights provides a pragmatic trade-off between storage overhead and deployment speed. For developers, understanding the responsibilities and constraints of each link in this chain is more valuable than memorizing any specific implementation. Future generations of models will inevitably introduce new computational paradigms, and a reusable adaptation methodology is the foundation for keeping pace with this evolution.