Kimi K3.1 Pro Achieves Day-0 Support on Domestic AI Computing Hardware

Background: Open-Weight Models and Domestic Computing Infrastructure

As large language models (LLMs) scale beyond the trillion-parameter mark, model inference imposes strict requirements on underlying hardware compute power, VRAM capacity, and software ecosystem maturity. Recently, a leading AI research institution officially released the complete model weights for Kimi K3.1 Pro. Leveraging a massive scale of 3 trillion parameters and an ultra-long context window supporting 1 million tokens, the model has quickly become a focal point in the open-weight community. According to third-party agent evaluation benchmarks, it ranks first among open-weight models and thirteenth overall, achieving leading results in dimensions such as programming, work assistance, and long-horizon agent tasks.

Simultaneously, the software ecosystem for domestic AI computing infrastructure is becoming increasingly mature. Represented by a domestic full-function GPU accelerator (model: AI Training and Inference Integrated Computing Card MTT S5000) and its accompanying MUSA software stack, domestic computing hardware is transitioning from being functional to being highly usable. The core value of this adaptation work lies in achieving “Day-0” support for newly released models on domestic hardware—meaning model weight release and hardware adaptation are nearly simultaneous. This significantly shortens the waiting period from model open-sourcing to actual deployment.

Model Architecture Analysis: 3 Trillion Parameters and Hybrid Attention

As the first official version of the K3 series, Kimi K3.1 Pro achieves breakthroughs in multiple technical dimensions. First, the model adopts a sparse attention mechanism (DSA), which is one of the core technologies enabling it to support a million-level context window. Compared to traditional dense attention, sparse attention selectively focuses on key subsets of the input sequence, significantly reducing computational complexity and VRAM consumption. This makes it possible to process ultra-long sequences with limited hardware resources.

Second, the model introduces an architectural design that fuses hybrid attention with visual computing. This means the model processes not only pure text data but can also jointly encode and infer visual information (like images) alongside text, supporting multimodal application scenarios. This feature imposes higher requirements on the inference framework: it must efficiently handle the logic of text autoregressive generation while concurrently supporting the convolution and attention mixed operations of the visual encoder.

Regarding performance benchmarks, the model scored 65.7% on Terminal-Bench 4.0 and 61.2% on the Long-horizon code generation benchmark Kimi Code Bench 3.0, showing a significant improvement compared to the previous generation K3 model’s 55.95%. These metrics indicate that large-scale post-training and reinforcement learning strategies have effectively enhanced the model’s planning and execution capabilities in multi-step tasks.

Adaptation Pathway: Rapid Migration via the MUSA Software Stack

A technical highlight of this adaptation work is that the team did not rewrite the underlying inference code for the new model. Instead, relying on the mature MUSA software stack, they achieved end-to-end integration from model loading and service startup to text and image inference pipelines. The MUSA software stack provides an abstraction layer highly compatible with the mainstream CUDA ecosystem at the programming model, runtime interface, and AI framework levels, forming the critical foundation for rapid adaptation.

Specifically, the adaptation followed this technical pathway:

1. Inference Framework Integration

The team adopted SGLang-MUSA as the inference service framework. As a high-performance LLM inference engine, SGLang inherently possesses the ability to adapt to various model architectures. Through MUSA backend extensions, the team mapped upstream model definitions to execute on domestic hardware without modifying the framework’s main logic. The framework also supports optimization technologies like Continuous Batching and Prefix Caching, effectively improving GPU utilization.

2. Operator Library and Compiler Toolchain Support

At the operator level, the adaptation leveraged the MATE operator library, Triton-MUSA, TileLang-MUSA, and the MTCC compiler. For standard operators appearing in the model (such as matrix multiplication, LayerNorm, and RMSNorm), the MATE operator library directly provides high-performance implementations. For computing paths unique to the new architecture (such as index computation and sparse matrix multiplication involved in DSA sparse attention), the team wrote custom operators using Triton-MUSA and TileLang-MUSA, compiling them into efficient native instructions via the MTCC compiler.

It is worth emphasizing that the MUSA software stack’s support for the Triton language significantly boosts operator development efficiency. Developers can write GPU kernels using high-level languages similar to Python without needing deep knowledge of underlying hardware details, drastically reducing the engineering effort required for new model adaptation.

3. Porting and Optimization of Key Computing Paths

Addressing the DSA sparse attention mechanism in Kimi K3.1 Pro, the adaptation team focused on solving challenges related to index partitioning and compute flow control. The index structure of DSA determines which pairs of tokens require attention computation, and this index is often dynamic. The team implemented the generation and parsing logic of DSA indices within the MUSA software stack and integrated it into the scheduling flow of the attention computation kernel, ensuring that long-sequence inference does not create performance bottlenecks due to index querying.

Regarding hybrid attention and visual computing, the team bridged the data path between image feature extraction and text embedding. This allows the model to uniformly schedule computing resources and complete cross-modal attention computation when inputting multimodal data.

MUSA Software Stack Ecosystem Strategy: Reuse and Optimization

The core design philosophy of the MUSA software stack is “compatible reuse, on-demand optimization.” Its architectural abstraction consists of multiple layers:

• Framework Layer: Supports the integration of mainstream AI frameworks like PyTorch and SGLang, distributing compute tasks through a backend adaptation layer.
• Compilation Layer: Provides the MTCC compiler and Triton-MUSA frontend, supporting automatic conversion from high-level languages to device instructions.
• Operator Layer: The MATE operator library provides basic and fused operators, while TileLang-MUSA allows developers to quickly build custom operator templates using declarative languages.
• Runtime Layer: Manages device and memory management, stream scheduling, and multi-card communication. It is compatible with NCCL-style communication primitives, supporting large-scale distributed inference.

This layered software stack design yields two direct benefits. First, during model adaptation, most standard operations can directly reuse mature interfaces, avoiding redundant development from scratch. Second, optimizations for new computing paths (like DSA) can be precisely targeted at the operator or compiler level—modifying exactly where needed—without affecting the stability of the overall framework.

Inference Performance and Deployment Considerations

From an actual deployment perspective, this adaptation has bridged the text and image inference pipelines, meaning developers can directly deploy image-question-answering applications on the MTT S5000 accelerator. For enterprise users, the following key points require attention during implementation:

• Memory Management: Even when stored at low precision (e.g., FP8/INT8), the weight file of a 3-trillion-parameter model far exceeds the VRAM of a single card. Actual deployment requires tensor parallelism or pipeline parallelism strategies to shard the model across multiple accelerators. The collective communication interfaces supported by the MUSA software stack enable efficient multi-card gradient synchronization and activation communication.
• Long-Sequence Inference Optimization: A 1-million-token context window implies that the storage overhead for the KV Cache could reach tens of gigabytes. During adaptation, combining the sparse characteristics of DSA, the team can further compress the memory footprint of the KV Cache, storing only the key values corresponding to sparse indices.
• Performance Tuning: Although Day-0 support has been achieved, performance tuning for specific business workloads must continue. It is recommended that users simulate real concurrent scenarios using stress-testing tools before deployment, observe key metrics such as GPU utilization, memory bandwidth, and Time-To-First-Token (TTFT), and fine-tune batch sizes and concurrent thread counts accordingly.

Future Outlook: Accelerating the Domestic Computing Ecosystem

The rapid adaptation of Kimi K3.1 Pro holds demonstrative significance. It proves that with the support of a mature software stack, domestic accelerators can quickly follow up on the evolution of cutting-edge global model architectures. Moving from “chasing models” to “releasing synchronously with models” marks a solid step forward for domestic AI infrastructure in usability and ecosystem completeness.

For developers, this means that when choosing domestic computing hardware to deploy large models in the future, they will no longer be hindered by long software adaptation cycles. As the MUSA software stack continues to improve its support for dynamic shape graphs, sparse computing, and multimodal models, domestic computing hardware is poised to become an out-of-the-box, preferred solution in more real-world business scenarios. Throughout this process, ecosystem development surrounding operator libraries, compilers, and distributed communication will continue to determine how fast and how far domestic computing platforms can advance.