Day-0 Adaptation of a Trillion-Parameter Model on a Domestically Developed Heterogeneous AI Computing Infrastructure

As large models scale into the trillions of parameters and evolve into complex agents capable of processing text, images, and other modalities, how efficiently the underlying compute infrastructure adapts to these models has become a decisive factor in how quickly AI applications reach production. Recently, engineers completed a Day-0 adaptation of an open-weight 3-trillion-parameter large model on China-developed heterogeneous AI chips and a full-stack software architecture, achieving end-to-end connectivity across model loading, service startup, and multimodal inference. The effort validated the technical capacity of the domestic hardware and software stack at extreme parameter scales and offers a practical methodology for enterprises pursuing private deployment and inference optimization of large models.

1. Why Day-0 Adaptation Matters—and What Makes It Hard

“Day-0 adaptation” means that the moment a mainstream large model releases its full weights, the underlying compute platform can already support its inference pipeline at a production-ready, engineering level. For enterprises seeking first-mover advantage, this capability dramatically shortens the window between a model’s release and its business launch.

Achieving Day-0 adaptation at the trillion-parameter scale, however, involves several deep technical challenges:

1. The memory wall and communication bottlenecks: Even under low-bit quantization, the weights of a 3-trillion-parameter model are enormous and must be split across many accelerator cards using tensor parallelism or pipeline parallelism, placing extreme demands on inter-card communication bandwidth.
2. Dynamic computation graph adaptation: Modern large models commonly adopt mixture-of-experts architectures with dynamic routing, so the computation path changes with each input. The underlying AI framework and operator library must therefore provide strong dynamic-shape support and efficient scheduling.
3. The multimodal fusion pipeline: The model must process not only text but also high-resolution images in real time. KV cache interactions between the vision encoder and the language model, along with cross-layer residual connections, require seamless heterogeneous compute support from the underlying hardware.

2. The Full-Stack Software Architecture: The Cornerstone of Rapid Migration

The full-stack software system played a decisive role in this adaptation. Because the highly compatible software stack aligns with mainstream ecosystems at the programming model, runtime interface, and AI framework levels, rapid migration of new models became possible. The entire process avoided tedious instruction-level hardware development and instead relied on mature framework interfaces and compiler toolchains for fast onboarding.

2.1 Building the Inference Service on SGLang

To handle the complex implementation of the upstream trillion-parameter model, the adaptation team built the core service on the SGLang inference framework, which is known for its RadixAttention prefix caching and efficient multi-card scheduling. On the heterogeneous compute foundation, the team rewrote SGLang’s backend device operation interfaces, smoothly porting scheduling logic originally built for the general-purpose GPU ecosystem to domestic accelerator cards.

2.2 A Deeply Optimized Operator Compilation System

Running open-source model code efficiently on specific hardware depends on a complete operator compilation and execution system. This adaptation covered several key components:

• MATE operator library: The core acceleration library for fundamental mathematical operators, deeply optimized at the instruction level for operations common in large models, such as matrix multiplication and LayerNorm.
• Triton and TileLang adaptation: By porting high-level operator programming languages such as Triton and TileLang to the domestic hardware, developers can write high-performance custom operators in Python-like syntax, greatly lowering the barrier to operator development on new architectures.
• MTCC compiler: Acting as the bridge between high-level operator expressions and low-level hardware instructions, the MTCC compiler handles instruction generation, memory allocation, and register scheduling, ensuring that new computation paths execute efficiently on the target hardware.

Together, this software stack covers framework execution, operator compilation, device runtime, and multi-card communication (including NCCL-compatible communication primitives), allowing model adaptation to maximize reuse of mature interfaces and achieve “adapt once, run in coordination across all cards.”

3. Tackling DSA Sparse Attention and the Hybrid Vision Compute Path

When a trillion-parameter model supports context windows of up to a million tokens, standard attention mechanisms cause memory consumption to grow quadratically and decoding latency to rise sharply. The model therefore introduces a sparse attention mechanism (DSA) and a hybrid vision compute architecture—the two toughest problems in this adaptation.

3.1 Low-Level Reconstruction of DSA Sparse Attention

For the DSA mechanism, the software stack had to deeply adapt index selection, KV cache layout, and the two core compute paths: Prefill and Decode.

• Index selection and KV cache reordering: In sparse attention, not all tokens participate in the attention computation. The system must rapidly extract and reorder KV cache entries in memory according to dynamic routing results. At the low level, optimized MATE sparse attention operators exploit the high bandwidth of HBM (high-bandwidth memory) to efficiently aggregate reads from non-contiguous memory.
• Dual-path optimization for Prefill and Decode: The Prefill phase is compute-intensive, so the team relied on the MTCC compiler’s operator fusion to execute QKV projection together with RoPE rotary position embedding. The Decode phase is memory-intensive, so the focus shifted to reducing cross-card KV cache read latency.

3.2 The Vision Pipeline: Fused KDA State and Cross-Layer Residuals

In multimodal inference, image features extracted by the vision encoder must be injected into the language model through cross-layer residual connections. This requires the software stack to manage not only the language model’s own KDA (Key-Value State Management) state but also to integrate seamlessly with the vision attention compute flow. By mapping vision features into a unified device memory space and using a unified operator scheduler for temporal control, the system built a complete end-to-end execution chain from image input through vision feature extraction to language model decoding.

4. Engineering Recommendations for Enterprise-Grade Deployment

Based on this Day-0 adaptation practice, enterprises deploying ultra-large multimodal models privately should follow these engineering guidelines:

1. Evaluate compute-to-memory ratios: A 3-trillion-parameter model is extremely sensitive to memory capacity during inference. In cluster planning, prioritize accelerator cards with large memory capacity and pair them with high-speed PCIe or Scale-up interconnects to reduce the communication overhead introduced by tensor parallelism.
2. Build standardized inference images: Use container technology to package the AI framework, the MATE operator library, and related compiler dependencies. Standardized images keep the runtime environment consistent across development, testing, and production, preventing inference accuracy anomalies caused by underlying library version differences.
3. Introduce automated monitoring and scheduling: After a multimodal inference service goes live, integrate monitoring components to track memory utilization, KV cache hit rates, and Prefill/Decode latency in real time. Use dynamic batching to adapt the batch size to actual request concurrency and maximize hardware throughput.
4. Leverage mature cloud platform capabilities: At the infrastructure level, container instances and object storage services from mature cloud platforms—such as Baidu AI Cloud—enable elastic scaling of inference services and fast loading of massive model weights, reducing overall operational complexity.

Conclusion

The Day-0 adaptation of a 3-trillion-parameter model demonstrates that building a high-performance heterogeneous AI computing foundation requires more than raw hardware—it depends on a full-stack software ecosystem that is highly compatible, easily extensible, and backed by a complete toolchain. Through low-level reconstruction of the sparse attention mechanism and the hybrid compute path, domestic heterogeneous compute has proven capable of running top-tier complex multimodal models efficiently. As hardware-software co-design continues to deepen, the cycle from model release to production deployment will keep shrinking, accelerating the intelligent transformation of industries.