Day-0 Adaptation of a 3-Trillion-Parameter Model on Domestic AI Chips: A Technical Path Analysis

1. Background: Why Day-0 Adaptation Becomes Critical for Large Model Deployment

As large model parameter scales climb from hundreds of billions to trillions, the time gap between model release and hardware adaptation has become a core bottleneck constraining AI application deployment. “Day-0 adaptation” refers to completing the full pipeline—from model loading and service startup to basic inference—on the same day a model’s weights are officially published or its API is opened. This tests not only raw chip compute power but also the software stack’s ability to rapidly respond to novel model architectures.

Recently, a cloud provider announced Day-0 adaptation of a 3-trillion-parameter model based on its AI training-and-inference integrated compute card and accompanying software stack. The model supports a 1-million-token context and adopts cutting-edge architectural designs including sparse attention mechanisms and MXFP4 expert weights. This adaptation case is highly representative—it demonstrates a complete technical migration path from community model code to a working inference service on a domestic compute platform.

2. Adaptation Challenges: Three Technical Barriers Introduced by New Architectures

2.1 Computational Path Reconstruction for Sparse Attention

The model’s sparse attention mechanism (DSA) differs fundamentally from conventional dense attention. In standard attention, each token computes attention weights with all other tokens in the sequence; sparse attention uses an index selection mechanism to compute only against a subset of key tokens, thereby controlling computational cost at a 1-million-token context length.

This mechanism imposes three specific requirements on hardware adaptation:

• Operatorization of index selection logic: The index selection algorithm must be moved from the framework layer down to the operator layer to fully leverage hardware parallelism;
• KV cache layout optimization: Under sparse mode, KV cache access patterns are highly irregular, requiring redesigned memory layouts to improve cache hit rates;
• Separate adaptation of Prefill and Decode paths: The Prefill stage is compute-intensive while the Decode stage is memory-access-intensive; their computation graphs differ significantly under sparse attention and must be optimized separately.

2.2 Memory Challenges of MXFP4 Expert Weights

In a 3-trillion-parameter model, the expert layers (MoE) account for the vast majority of parameters. The model stores expert weights in MXFP4 format—a 4-bit precision microscaling format that reduces storage footprint by 75% compared to FP16. However, the low-precision format also introduces new problems:

• During inference, MXFP4 must be dynamically converted to compute precision (e.g., FP8), and the conversion overhead is non-negligible;
• Cross-layer reuse of expert weights requires fine-grained workspace management to avoid fragmentation from frequent allocation and deallocation;
• An offline conversion approach that pre-generates full FP8 weights would consume substantial additional storage and sacrifice precision flexibility.

2.3 Execution Pipeline with Multi-Component Coordination

The model is not a single attention structure but integrates KDA state management, cross-layer residual connections, and a visual attention module. These components are interdependent in the computation graph—failure to adapt any single component breaks the entire inference pipeline. Therefore, adaptation work must cover the complete execution path from the Embedding layer to the output layer.

3. Technical Approach: Migration Path from Community Code to Working Inference Service

3.1 Framework Adoption Based on SGLang-MUSA

The adaptation team chose to build on SGLang-MUSA to host the upstream model implementation. SGLang, as a serving framework for large model inference, provides optimizations such as RadixAttention, while the MUSA software stack maps framework-level computation graphs onto domestic hardware.

The core philosophy of this path is: do not rewrite model logic; instead, migrate the computational path through the compiler and operator libraries. Specifically:

1. Model code parsing: SGLang loads the community model definition and generates a hardware-agnostic intermediate representation;
2. Operator mapping: The MATE operator library maps key computation nodes in the intermediate representation to hardware-optimized operators;
3. Compilation optimization: The MTCC compiler performs fusion, reordering, and memory allocation optimization on the computation graph;
4. Service packaging: The final output is an inference service runnable on the compute card.

3.2 Four-Layer Adaptation Strategy for Sparse Attention

For the DSA mechanism, the MUSA software stack completed the following adaptation work:

Layer 1: Operatorization of index selection
The index selection logic was implemented as a MATE sparse attention operator, leveraging hardware vectorization capabilities to accelerate the Top-K filtering process.

Layer 2: KV cache layout restructuring
For sparse access patterns, a hybrid blocked-plus-index layout was adopted, pinning frequently accessed KV blocks in high-speed cache regions.

Layer 3: Prefill/Decode path separation

• Prefill stage: A compute-intensive parallel strategy maximizes matrix compute unit utilization;
• Decode stage: A memory-access-optimized strategy reduces KV cache read latency.

Layer 4: Multi-component fusion
Sparse attention, KDA state management, cross-layer residuals, and visual attention are orchestrated into a complete execution pipeline, with cross-operator fusion performed by the MTCC compiler to reduce intermediate result memory reads and writes.

3.3 Online Conversion Scheme for MXFP4 Expert Weights

For MXFP4-format expert weights, the adaptation team adopted an “online conversion + cross-layer reuse” strategy:

• Online conversion: MXFP4 weights are converted to FP8 format in real time before expert computation, with the conversion process overlapped with the compute pipeline to hide conversion latency;
• Cross-layer FP8 workspace reuse: Expert computations across different layers share a single FP8 workspace, avoiding separate weight buffer allocation per layer;
• No offline pre-generation required: Eliminating the step of generating full offline FP8 weights saves storage space while preserving precision-tuning flexibility.

The key to this scheme is that conversion overhead is masked by the compute pipeline, while workspace reuse significantly reduces memory footprint.

4. Engineering Practice Highlights

4.1 Compiler and Operator Library Collaboration

The MTCC compiler handles global computation graph optimization, while the MATE operator library provides hardware-optimized base operators. Their collaboration works as follows:

Model computation graph → MTCC compiler → Operator fusion/memory planning → MATE operator invocation → Hardware execution

In practice, the compiler must be able to recognize new patterns such as sparse attention and correctly invoke the corresponding MATE operators.

4.2 Balancing Precision and Performance

The MXFP4 online conversion scheme must balance precision and performance:

• Conversion precision directly affects model output quality and must be validated against a calibration set;
• Conversion speed affects end-to-end latency and must be hidden through pipeline overlap;
• Workspace size affects concurrency capacity and must be configured appropriately based on memory capacity.

4.3 Validation and Tuning Workflow

Day-0 adaptation validation should cover:

1. Functional validation: Whether model loading, service startup, and basic text/image inference pipelines work end to end;
2. Precision validation: Whether output differences from the reference implementation fall within an acceptable range;
3. Performance validation: Whether Prefill throughput, Decode latency, and concurrency capacity meet service requirements;
4. Stability validation: Whether long-running operation exhibits memory leaks or precision drift.

5. Summary and Outlook

Day-0 adaptation of a 3-trillion-parameter model is essentially a test of the software stack’s ability to rapidly respond to novel model architectures. Three reusable lessons can be drawn from this adaptation practice:

1. Framework adoption beats rewriting: Hosting community model code on a mature inference framework and achieving hardware migration through the compiler and operator libraries can substantially shorten the adaptation cycle;
2. Sparsity and low precision are mandatory: As model scales continue to grow, sparse attention and low-precision weights will become standard, and software stacks need to proactively build related operator and conversion capabilities;
3. Full-pipeline coordination is key: Adapting a single component is insufficient to support a complete inference service; end-to-end optimization from the Embedding layer to the output layer is essential.

For developers, understanding this adaptation path helps deploy large-scale models more efficiently on domestic compute platforms; for enterprise users, Day-0 adaptation capability means being able to bring the latest model capabilities into business scenarios immediately, capturing the technology dividend window.