Domestic Full-Stack Adaptation Breakthrough: Day-0 Support for 3-Trillion-Parameter Kimi Model, Connecting Text and Image Inference Pipelines

Background: The “Last Mile” Challenge from Model Release to Compute Deployment

Large language model development is entering a new phase: parameter counts are moving from hundreds of billions to trillions, and task modalities are expanding from pure text to mixed text-image reasoning. However, releasing a strong set of model weights is only the starting point. The real industrial challenge lies in running massive models efficiently across heterogeneous compute environments. This is especially true for models built on new architectures—such as hybrid attention mechanisms and sparse inference paths—where bridging community code and production-grade inference services often requires overcoming a deep engineering gap.

Recently, a well-known model team officially released the weights of its new flagship large model. This model is widely recognized as one of the most capable trillion-parameter models in the open-weight ecosystem, supporting ultra-long context windows and demonstrating capabilities comparable to top closed-source models in code generation, agent tasks, long-range planning, and financial analysis. More notably, the model introduces a Sparse Attention mechanism and a distinctive visual encoding pathway, posing unprecedented challenges for compute infrastructure adaptation.

Against this backdrop, a domestic GPU vendor announced that its self-developed high-performance training-and-inference integrated compute card and full-stack software platform had achieved “Day-0” adaptation for this model. This means that on the day the model weights were published, the inference pipeline was fully operational, supporting basic dual-modality inference for both text and images. This progress not only signals improved maturity of domestic compute software stacks but also provides a new template for rapidly deploying ultra-large-scale models on domestic hardware.

1. Three Core Challenges of Ultra-Large-Scale Model Inference

Before discussing the adaptation approach, it is necessary to clarify the hard problems that a 3-trillion-parameter model faces on the inference side. Unlike common models with tens or hundreds of billions of parameters, the inference bottleneck here is not simply a matter of memory capacity—it involves multidimensional coupling across system architecture, computation patterns, and scheduling strategies.

1.1 Memory Bottlenecks and Heterogeneous Storage Scheduling

3 trillion parameters means that storing model weights in conventional FP16 precision alone requires approximately 6TB of memory. A single accelerator card clearly cannot accommodate this, forcing engineering teams to carefully design multi-card parallelism, tensor parallelism/pipeline parallelism, and KV Cache management. Simply partitioning model parameters across multiple cards is not enough—dynamically managing KV Cache memory occupancy during the PreFill and Decode phases and reducing memory fragmentation are key to service stability.

1.2 Computational Pattern Shifts Brought by Long Context

Supporting million-level token contexts means the computational complexity of the Attention mechanism grows exponentially. Traditional full attention mechanisms not only incur enormous computational overhead in long-context scenarios but also strain memory access bandwidth. To address this, modern frontier models introduce sparse attention mechanisms—selectively attending to certain key tokens (such as specific positional indices or nearby windows) rather than computing pairwise attention weights across all tokens. This mechanism changes the topology of the computation graph, meaning the underlying operator library can no longer rely on standardized matrix multiplication and must implement highly customized sparse operators.

1.3 Fusion of Multimodal Heterogeneous Compute Paths

Beyond text processing, the model also needs to support image inference. Visual encoders typically use network structures entirely different from text decoders (such as hybrid convolution-attention structures). Seamlessly feeding embeddings generated by the visual encoder into the text decoder’s attention stream—without disrupting established KV Cache state management—is an extremely difficult engineering optimization point, whether on single or multiple cards. If this path fails, the model degrades into a pure text system and loses multimodal capability.

2. Key Support for Day-0 Adaptation: The Full-Stack Software Toolchain “Combination Punch”

Facing the above challenges, the domestic chip vendor’s ability to complete adaptation in a short time stems from its self-developed “full-stack software stack system”—an integrated, end-to-end optimization from low-level compilers to high-level AI frameworks. This system delivered three core values in this adaptation.

2.1 High-Compatibility Migration: “Seamless Porting” from Community Frameworks to Self-Developed Hardware

In the large model inference space, mainstream community inference frameworks (such as SGLang and vLLM) have become de facto standard implementations. However, the underlying operators of these frameworks are typically deeply optimized for a specific vendor’s CUDA ecosystem. Facing a new hardware architecture, rewriting frameworks from scratch would be prohibitively expensive.

The vendor’s software stack solution lies in its multiple frontend compatibility layers. At the programming model layer, compatibility with mainstream heterogeneous parallel programming interfaces means developers do not need to rewrite core kernel logic. At the runtime interface layer, abstraction of device management, memory allocation, and task scheduling shields underlying hardware differences. At the AI framework layer, deeply optimized operator libraries (such as the vendor’s MATE operator library) and accelerator backends adapted for different programming languages (such as Triton-MUSA and TileLang-MUSA) are provided. This combination allows developers to migrate community model Python code almost unchanged, while computation graph execution is automatically mapped to self-developed hardware.

2.2 Engineering Practice of Operator Migration: The Compiler’s “Last Mile” Decoding

Although high-level frameworks provide convenience, the model’s underlying computation ultimately still lands on specific operator kernels. In this model, the newly introduced hybrid attention computation path is not a standard operator and cannot be called directly from the operator library. The team leveraged its self-developed compiler toolchain (MTCC compiler), combined with Triton Dialect and TileLang DSL (domain-specific language), to achieve “hand-written-level” performance optimization of hot kernels at the compiler level.

This process involves two key steps: first, the compiler must correctly parse the new architecture’s computation dependency graph—including sparse index computation and non-contiguous KV Cache layout reads and writes; second, during the auto-tuning phase, the compiler must automatically select optimal loop unrolling factors and data tiling strategies based on the self-developed hardware’s memory bandwidth and compute array size. Through this approach, Python-level logic in community models is efficiently “decompiled” into low-level machine code, with performance overhead kept extremely low.

2.3 Critical Path Reconstruction: Deep Stitching of Sparse Attention and Vision Computation

Adaptation of the model’s most distinctive feature—the sparse attention (DSA) mechanism—was the top priority of this Day-0 cycle. Although production-grade inference frameworks (such as SGLang) provide framework hooks for sparse attention, their internal implementations often depend on specific KV Cache layouts.

The chip vendor’s software stack adaptation logic is more systematic:

• Index Selection and State Management: Within the MUSA software stack, the team designed an independent index management module for the DSA mechanism to dynamically record token positions that need attention within the current context window. This module generates sparse masks during the Prefill phase and dynamically updates key-value pair selection strategies during the Decode phase.
• Operator Customization: A specially optimized sparse attention operator (within its MATE operator library) supports different sparsity patterns and, targeting the hardware architecture’s locality characteristics, rearranges KV Cache read/write patterns to reduce DRAM (dynamic random-access memory) access counts.
• Cross-Layer Residuals and Vision Fusion: For image inference, patch embeddings generated by the visual encoder need to be concatenated with text token sequences. The software stack uses a “multimodal attention state management” mechanism to insert the visual portion’s KV as an independent sequence into the attention cache stream. Simultaneously, cross-layer residual connections ensure that image patch information does not decay as it propagates through deeper network layers, enabling seamless joint inference of text and images.

3. From Adaptation to Delivery: The Performance Shape of Inference Services

Completing model loading and operator mapping is only the first step. The real test is providing a stable, low-latency inference service. In this Day-0 adaptation, the chip platform demonstrated its potential to support “production-grade services.”

Based on its MUSA software stack, the team abstracted underlying hardware into a unified device resource pool supporting multi-card parallel communication. During the service startup phase, an optimized model loader enabled distributed loading of model weights at the thousand-card cluster scale. During the inference execution phase, refined scheduling granularity within the Pipeline ensured high utilization of compute units when executing sparse Attention. Ultimately, the platform successfully supported stable operation of the model on MTT S5000 compute cards and exposed standard OpenAI-compatible API interfaces, allowing upper-layer applications (such as agent platforms or chatbots) to integrate seamlessly.

4. Review and Outlook: The Construction Logic of Domestic Compute Ecosystems

The significance of this rapid adaptation event goes far beyond getting a single model to run. It reflects that the domestic AI software and hardware ecosystem is undergoing a critical transition from “usable” to “good to use.”

In the past, the shortcoming of domestic compute ecosystems was software fragmentation—developers needed to write multiple sets of code for different hardware. Now, by providing a full-stack software stack compatible with mainstream AI ecosystems and equipped with high-performance compilation capabilities, domestic solutions have been able to shorten adaptation cycles from weeks to “Day-0.” For open-source large models with extremely short lifecycles and rapid version iteration, this is a critical capability.

Of course, we must remain objective. Achieving basic inference pipeline connectivity for text and images is merely the starting point of large-scale commercialization. The next challenges lie in further optimizing KV Cache memory utilization in high-concurrency inference under long-context scenarios; ensuring that sparse attention operators do not lose precision under low-bit quantization; and competing on performance with commercial inference engines (such as the industry standard represented by TensorRT-LLM). These are the hard battles of Day-1 and Day-2 that will follow Day-0.

For the developer community, this adaptation case provides a valuable practical template: when large model architectures evolve again, a software stack with strong compatibility, fine operator granularity, and a mature compiler toolchain is the best defensive investment against the endless stream of algorithmic changes. After all, in the AI compute era, hardware determines the ceiling of compute power, while software determines which layer of that ceiling you can actually reach.