Deploying Large-Scale LLMs on the Ascend Platform: Inference and Adaptation Practices for K3.1 Pro

Background: New Challenges in Large-Scale Model Inference

As the parameter scale of large language models advances toward the trillion-level, the computational power and memory pressure during the inference phase grow exponentially. K3.1 Pro, the first model in the K3.1 series, features 2.8 trillion parameters and natively integrates visual understanding capabilities, supporting a context window of up to a million tokens. This scale dictates that even high-performance GPU clusters require meticulously designed distributed inference solutions to meet the latency and throughput requirements of online services.

From an engineering perspective, the core challenge of deploying massive models lies not merely in the total parameter count, but in the operator fragmentation caused by diverse model architectures. Different models may employ fundamentally distinct attention mechanisms, expert routing strategies, and multimodal fusion methods. This requires the inference framework and underlying operator libraries to possess high extensibility and adaptation efficiency.

Model Architecture Analysis: Computational Characteristics of Hybrid Linear Attention

At the computational architecture level, K3.1 Pro adopts a hybrid linear attention design that organically integrates two attention mechanisms. The first is the KDA linear attention layer, whose core idea is to approximate the standard softmax attention calculation using kernel functions, reducing computational complexity from quadratic to linear relative to sequence length. The second is the DSA sparse attention layer, which introduces sparsity patterns to focus on key tokens, thereby reducing computational overhead in long-context scenarios.

The introduction of this hybrid architecture brings several changes to underlying computational characteristics:

The computation pattern of the KDA linear attention layer is dominated by matrix multiplication and element-wise operations. Its high computational density makes it highly suitable for efficient execution on Tensor Cores. However, the introduction of the linear attention kernel function is accompanied by additional computation of normalization factors, requiring operator designs to holistically consider numerical stability.

The DSA sparse attention layer introduces greater adaptation complexity. The computation pattern of sparse attention is no longer a standard matrix multiplication but selectively calculates attention scores based on sparse masks. This irregular computation pattern requires the operator implementation to dynamically handle sparse indices while fully leveraging the hardware’s high-bandwidth memory access capabilities.

Additionally, the model incorporates a Mixture of Experts (MoE) architecture, meaning that during inference, inputs must be dynamically routed to different expert networks based on token features. Multimodal inference capabilities require the model to simultaneously process text and visual inputs, involving feature alignment and fusion computations between the visual encoder and the language model.

Ascend Platform Adaptation Methodology: From Model Analysis to Operator Co-optimization

In the practices of the Ascend inference team, model adaptation is not a simple operator translation process but a systematic engineering effort spanning model architecture analysis, operator development, framework adaptation, and service validation. For the adaptation of K3.1 Pro, the team leveraged experience accumulated from previous K3 model versions, proactively conducting model structure decomposition and computational graph analysis. This significantly shortened the time window from model release to inference service launch.

Core Operator-Level Optimizations

Centered around the new computational paths of K3.1 Pro, operator-level optimization focused on the following key areas:

Historical Information Filtering (KV Cache Management and Filtering): A million-level context window places extreme demands on KV Cache storage capacity. During inference, the Key and Value tensors of historical tokens must be stored in high-speed cache. During the adaptation process, the Ascend team optimized the KV Cache storage layout by adopting a chunked storage strategy to reduce memory fragmentation. They also implemented efficient historical information filtering logic to quickly locate the set of historical tokens relevant to the current generation step.

Sparse Attention Computation: The core operators of the DSA sparse attention layer received specialized optimization on the Ascend platform. The optimization approach included preprocessing sparse masks and rearranging indices, transforming irregular memory access patterns into more regular block-wise access to improve data locality. During underlying computation, the vectorized instructions of the Ascend compute cores were utilized to efficiently execute the selected attention score calculations.

Preprocessing and Residual Computation: Multimodal inputs require preprocessing through the visual encoder and text embedding layers, a process involving massive tensor dimension transformations and normalization operations. The Ascend operator library performed fusion optimizations for these common computation patterns, reducing kernel launch overhead. The fused processing of residual computations effectively reduced the number of memory read/write operations, enhancing the efficiency of the overall computational pipeline.

Framework-Level Service Capabilities

Building upon operator optimizations, collaborative adaptation at the framework layer is equally indispensable. This specifically includes:

Distributed Cache Capability: Faced with a million-token context length, the memory of a single card is obviously insufficient to hold the entire KV Cache. The Ascend inference solution employs a distributed caching strategy, storing KV Cache shards across the memory of multiple compute cards. Coupled with optimizations from the high-performance collective communication library, it ensures that the data exchange latency between compute nodes remains within a controllable range.

Long-Context Inference Scheduling: During long-context inference, the computational characteristics of the prefill phase and the decode phase differ significantly. The prefill phase requires high-throughput matrix computation, while the decode phase is limited by the data dependencies of single-step generation. The inference framework adopts different parallel strategies and scheduling algorithms for these two phases. The prefill phase utilizes a combination of tensor parallelism and data parallelism, while the decode phase combines batch scheduling with KV Cache reuse mechanisms to maximize node utilization.

Practical Key Points for Inference Service Deployment

Based on the inference deployment practices of K3.1 Pro, several key technical points can be extracted, offering universally applicable reference value for all large-scale model inference services:

Coordinated Planning of Memory and Compute Resources

For model inference with a million-level context window, memory capacity is the primary bottleneck. Besides model weights and gradient storage, the dynamic growth of the KV Cache is the main variable in memory consumption. In deployment practices, it is recommended to pre-calculate the upper limit of KV Cache occupancy based on the maximum input length and batch size, reserving sufficient memory margins to accommodate changes during the generation phase. If a multi-card parallel inference solution is used, attention must also be paid to the balanced distribution of KV Cache shards across cards to prevent a single card from becoming a hotspot and causing latency bottlenecks.

Selection and Tuning of Computation Parallel Strategies

For a 2.8-trillion-scale hybrid linear attention model, a single parallel strategy is insufficient for full-link inference requirements. Industry practices indicate that combining tensor parallelism, pipeline parallelism, and expert parallelism (distributing experts across multiple cards and dynamically aggregating based on routing results) often yields better compute utilization. On the Ascend platform, developers need to find the optimal parallel configuration by considering the specific model structure (e.g., number of experts, number of attention heads, hidden dimensions) and hardware topology (e.g., inter-card interconnect bandwidth). This process requires the assistance of fine-grained profiling tools to identify the specific locations of communication and computation bottlenecks.

Optimization of Multimodal Input Processing

K3.1 Pro possesses native visual understanding capabilities, meaning the inference service must support the processing of both text and image inputs. In actual deployment, image inputs often require preprocessing by a visual encoder to be converted into visual token sequences. The latency control of this process and its coordinated scheduling with the text processing decode pipeline represent a noteworthy optimization point. Practical solutions include: conducting batch-level parallel inference for the visual encoder, and fusing the attention computation of visual tokens with that of text tokens during the decoding process to avoid performance losses caused by operator switching.

Canary Release and Elastic Scaling

For cloud deployment scenarios, ensuring the stability of the inference service is critical. It is recommended to adopt a progressive release strategy, first enabling the new model service on a small number of instances. After verifying service stability by monitoring core metrics such as Time To First Token (TTFT), generation throughput, and memory usage, traffic can be gradually scaled up. Concurrently, reasonable elastic scaling strategies should be set based on load conditions. In million-level context scenarios, a single long-connection request may persist for tens of seconds, requiring the load balancer to be connection-state-aware to avoid frequent migrations.

Application Scenarios and Conclusion

The combination of a million-level context window and multimodal capabilities allows K3.1 Pro to demonstrate significant application potential in various complex scenarios. In software development, the model can comprehensively utilize massive codebases, design documents, and multi-turn interaction history from past issues, combining tool invocation capabilities to accomplish multi-step tasks like code review, bug localization, and automatic repair. In enterprise knowledge processing, the model can process hundreds of pages of contracts, financial statements, or research reports in a single pass, parsing chart data through visual understanding to provide deep decision-support information.

This adaptation practice illustrates that the competitiveness of a large-scale model inference platform is reflected not merely in the stacking of hardware compute power, but more importantly in the rapid adaptation capability of the operator library and framework to cutting-edge model structures. The engineering accumulation within the inference stack—from model structure parsing and operator development to distributed scheduling and service deployment—has become a key element of differentiation for compute platforms in an era where model iteration speeds are continuously accelerating. For developers, understanding underlying computational characteristics and making targeted adjustments to deployment strategies are essential prerequisites for unlocking the performance potential of large-scale models.