Local Inference of Trillion-Parameter Open-Source Models: Adaptation Practices and Performance Observations on Domestic AI Accelerators

Background: Computing Supply and Demand in the Trillion-Parameter Era

As Large Language Model (LLM) parameter scales leap into the trillions, the demands on underlying hardware for memory capacity, compute density, and interconnect bandwidth have grown exponentially. The industry broadly faces a dilemma: while top-tier closed-source models offer exceptional performance, balancing data security with deployment costs is difficult. Meanwhile, although the open-source community continues to release highly capable model weights, the operational thresholds often exclude most enterprise users. Particularly when model parameters reach the 3-trillion mark, even with low-bit quantization, single-GPU inference cannot meet memory requirements. Complex strategies such as multi-GPU parallelism, tensor parallelism (TP), and pipeline parallelism (PP) become mandatory.

Against this backdrop, the adaptation capability of domestic computing stacks has become a key metric for measuring their industrial value. Recently, a 3-trillion-parameter open-source model named K3.1 Pro was officially released, drawing significant industry attention. Touted as the “most capable 3-trillion-parameter open-source model to date,” it natively supports an input context length of up to 1 million tokens. Notably, a domestic GPU vendor achieved Day-0 adaptation for this model using its integrated AI training and inference accelerator cards and a self-developed software stack, enabling local inference capabilities on the very day the model was open-sourced. This milestone marks a substantial step for domestic computing infrastructure in supporting ultra-large-scale open-source models.

Model Architecture Characteristics and Compute Profiling

For any hardware adaptation effort, understanding the target model’s architecture is the first step. K3.1 Pro is not merely a stacking of parameters; its design presents several distinct characteristics:

First, an ultra-deep Mixture of Experts (MoE) architecture. Within the 3 trillion total parameters, the activated parameters are typically kept at a low ratio (e.g., 10% to 15%). This means the actual computation during inference is far less than the total parameter count. However, the expert routing mechanism introduced by the MoE architecture imposes stringent requirements on memory bandwidth and All-to-All communication patterns. Each token might need to activate dozens of experts at each layer. If these experts are distributed across different compute cards, the cross-card communication latency will directly dictate the upper limit of the generation speed.

Second, long context support (1 million tokens). Supporting a million-level context means the model must handle extremely large Key-Value (KV) caches during inference. Assuming a single-layer head dimension of 128 and 80 layers, the KV cache size for a single sequence could reach hundreds of gigabytes or even terabytes. This forces the inference system to implement efficient KV cache management, paged storage, and intelligent offloading strategies; otherwise, video memory (VRAM) will be rapidly exhausted.

Third, capability focus post-reinforcement learning. The model has undergone large-scale post-training and reinforcement learning, demonstrating outstanding performance in code generation, 3D creation, financial research, knowledge QA, and long-horizon Agent tasks. Among these, long-horizon Agent tasks require multi-turn planning and tool calling. The inference mode shifts from single-turn text generation to complex multi-step decision-making processes, placing higher demands on the stability and response latency of the inference service.

From a compute characteristic perspective, K3.1 Pro is a typical “memory-hungry” workload. Its bottleneck lies not entirely in peak compute (FLOPS) but rather in memory capacity, memory bandwidth, and inter-card communication efficiency. This has historically been a criticized shortcoming of domestic computing stacks, making the adaptation process technically challenging.

Adaptation Path and Engineering Practices of the MUSA Software Stack

Faced with these model characteristics, adaptation is not simply a matter of loading and running the model. It requires a full-stack overhaul involving the compiler, runtime, communication libraries, and inference engine. Relying on its self-developed MUSA software stack, the vendor implemented the following adaptation strategies around critical inference paths:

1. Memory Management and KV Cache Compression

To address the KV cache explosion caused by million-level contexts, the adaptation team implemented a multi-level memory management mechanism at the inference engine level:

• Paged KV Cache: Drawing inspiration from operating system virtual memory management, the KV Cache is divided into fixed-size pages allocated on demand, rather than pre-allocating a large contiguous block of VRAM. This design significantly improves memory utilization, making it possible to support long sequences within limited VRAM.
• Quantization-aware KV Cache Compression: Leveraging the INT8/FP8 compute capabilities supported by the S5000 accelerator cards, low-bit-width quantization is applied to KV Cache storage. Within an acceptable range of precision loss (typically less than 1% perplexity loss), the cache footprint is compressed to one-half or even one-quarter of its original size.
• VRAM/CPU RAM/NVMe Three-tier Offloading: When sequence lengths extremely exceed VRAM capacity, the system can migrate the KV values of infrequently used historical tokens to CPU memory or NVMe storage, dynamically reloading them when needed. This strategy trades some latency for the feasibility of handling ultra-long contexts.

2. Deep Tuning of Distributed Parallelism Strategies

3 trillion parameters cannot be loaded onto a single card; multi-card collaboration is mandatory. The engineering depth demonstrated by the MUSA software stack here includes:

• Hybrid Parallel Strategy Orchestration: For MoE models, combining standard Tensor Parallelism (TP) and Pipeline Parallelism (PP) is insufficient. The solution introduces an Expert Parallelism (EP) dimension—distributing different experts across different compute cards, paired with grouped All-to-All communication primitives to route tokens to the nodes hosting the corresponding experts. By adjusting the TP/EP dimension combinations, the system finds the optimal balance between memory footprint and communication overhead.
• Communication Topology-aware Task Scheduling: The interconnect topology underpinning the MTT S5000 is not fully symmetric. When assigning experts, the scheduler must be aware of the physical link bandwidth differences between cards, placing frequently interacting expert groups within directly connected nodes to reduce cross-node communication pressure. According to test data released by the adaptation team, optimized communication time was reduced by approximately 30% under specific Agent workloads.

3. Streamlining the Inference Engine and Operator Fusion

The high时效性 of a Day-0 adaptation forces the engineering team to reuse existing operator libraries rather than writing CUDA equivalents from scratch. To this end, the MUSA compiler focused on implementing:

• High-dimensional Operator Auto-fusion: For fusion patterns involving RMSNorm, SwiGLU activation functions, and attention computations, the compiler automatically identifies adjacent compute nodes and generates fused kernels, reducing VRAM read/write overhead caused by multiple kernel launches.
• Sparse MoE Operator Optimization: The sparse expert selection in MoE layers is essentially large-scale matrix multiplication, but it only needs to be executed for the selected expert rows. The adaptation utilizes sparse computation templates to avoid invalid computations on unactivated experts, significantly boosting overall compute throughput.

Benchmark Performance: Rational Interpretation and Evaluation Framework

According to public test data, K3.1 Pro scored 65.7% on Terminal-Bench 4.0 and 61.2% on Kimi Code Bench 3.0 Long-horizon, a significant improvement over the previous generation K3 model’s 55.95%. While these numbers are impressive, when evaluating adaptation quality, more attention should be paid to the following three types of validations:

Functional Correctness Validation: Whether the model’s output coherence, knowledge accuracy, and instruction-following capabilities are consistent with the reference architecture. This typically requires selecting hundreds of test prompts covering various domains for item-by-item comparison, allowing for minor fluctuations but no systematic degradation.

Performance Degradation Testing: Under the same model weights, batch sizes, and input lengths, comparing the end-to-end inference latency and throughput of the domestic computing stack against mainstream accelerators. Ideally, the performance gap should be kept within 20%; otherwise, user experience will be severely impacted.

Long-term Stability Stress Testing: Running continuously at full load for several hours to observe whether memory leaks, communication timeouts, or thermal throttling occur. Especially for Agent-type tasks, it is necessary to verify that the system does not crash due to resource fragmentation during continuous inference processes spanning tens of thousands of steps.

It must be objectively noted that current public information only states that the model can “run stably” on the MTT S5000, lacking detailed comparative data across the aforementioned dimensions. For enterprise users planning to adopt domestic computing solutions, it is recommended to conduct Proof of Concept (PoC) tests using their own business workloads, focusing on actual throughput in long-context scenarios, Quality of Service (QoS) fluctuations under high concurrent requests, and migration compatibility for future model version upgrades.

Domestic Computing: The Key Leap from Usable to Effective

From an industry perspective, the symbolic significance of this Day-0 adaptation far exceeds the technology itself. For an extended period, domestic GPUs faced not a deficit in single technologies, but a weak ecosystem. Every new model release often required weeks or even months to adapt, by which time the model’s hype had faded. Achieving Day-0 adaptation relies on the software stack’s ability to provide standardized abstractions for mainstream model architectures.

A healthy technology ecosystem should possess the following capabilities: rapid adaptation to new models (time costs measured in days), seamless compatibility with mainstream training/inference frameworks, a comprehensive performance tuning toolchain, and an active developer community. This practice indicates that the domestic computing stack is transitioning from “barely runnable” to a crucial stage of “worry-free deployment.”

However, it is also necessary to guard against the misleading nature of “parameter scale worship.” The successful adaptation of a 3-trillion-parameter model does not mean all large model workloads can run efficiently on domestic hardware. Enterprise decision-makers must still return to their business needs, comprehensively weighing model efficacy, deployment costs (including hardware procurement and operations), and data compliance.

Conclusion and Future Outlook

By deconstructing the K3.1 Pro model adaptation case, this article outlines the complete technical path for deploying ultra-large-scale deep learning models on a domestic computing stack. From MoE architecture analysis to memory management design, and from hybrid parallelism strategies to benchmark interpretation, each step embodies profound and reusable engineering methodologies.

Looking ahead, as model scales continue toward the 10-trillion parameter mark and new paradigms such as multi-modal inputs and long-horizon autonomous agents emerge, adaptation challenges for domestic computing stacks will become even more severe. Only by continuously honing the generalization capability and optimization depth of the software stack can a true moat be established in the long-term competition for computing independence. For technical teams, regardless of how underlying hardware evolves, understanding model compute characteristics and mastering system-level performance analysis methodologies remain enduring core competencies.