Deploying Trillion-Parameter LLMs on Domestic GPUs: A Practical Look at Inference Engineering

As model parameter counts cross the trillion threshold, the engineering complexity of LLM training and inference undergoes a fundamental shift. Recently, a large language model with three trillion parameters was released as open source and achieved Day-0 compatibility with a domestic AI training-inference accelerator. Day-0 compatibility means that at the exact moment the model was open-sourced, the hardware platform and software ecosystem were already capable of running it directly. This represents a notable milestone in the drive toward a self-sufficient domestic LLM hardware and software stack.

It is worth noting that three trillion parameters is not a number that can be handled by simply scaling up existing approaches. Growth in parameter count leads to nonlinear increases in memory footprint, sharply rising communication overhead, and significantly greater difficulty in controlling inference latency. This article sets aside specific brand narratives and focuses purely on the technical perspective, offering an in-depth analysis of the model’s core architectural characteristics, the key challenges in migrating the inference path, and the adaptation strategies employed across the domestic software stack.

1. Engineering Challenges Posed by a Three-Trillion-Parameter Model

When model scale jumps from hundreds of billions to three trillion parameters, traditional single-GPU or even single-node inference solutions become entirely inadequate. First, the storage requirement for model weights reaches hundreds of gigabytes. Even the accelerator with the largest memory capacity available today cannot load the complete model into a single device. This necessitates tensor parallelism, pipeline parallelism, or a hybrid strategy that distributes model shards across multiple compute devices.

Second, three-trillion-parameter models typically adopt sparse activation or Mixture-of-Experts (MoE) architectures. While these architectures reduce per-token computation relative to the total parameter count, they introduce complex expert routing and load-balancing problems. During distributed inference, different expert modules may be placed on different compute nodes. Tokens that need to be routed across nodes during inference place extremely high demands on interconnect bandwidth and latency.

Long-context support is another defining feature of this class of models. The model in question advertises a context window of one million tokens. This means that during inference, the memory occupied by the Key-Value Cache (KV Cache) grows linearly—or even superlinearly—with sequence length. In long-sequence scenarios, KV Cache memory usage often far exceeds the memory required for the model weights themselves, forcing developers to adopt optimizations such as PagedAttention, KV Cache quantization, and context compression.

2. Core Logic of Inference Path Migration and Adaptation

For the challenges described above, the central approach in this adaptation effort was not a simple model format conversion. Instead, it involved a systematic reconstruction of the inference path based on the hardware’s underlying instruction set and computational characteristics.

2.1 Key Adaptation Points in the Software Stack

The software stack plays a critical bridging role: it accepts computational graphs exported by various deep learning frameworks and maps them onto the general-purpose compute cores and dedicated matrix units of the accelerator. The adaptation process primarily involves three layers:

• Operator-level migration: LLM inference depends heavily on fundamental operators such as general matrix multiplication (GEMM), LayerNorm, Softmax, RMSNorm, and rotary position encoding (RoPE). During the initial adaptation phase, the engineering team needed to validate the implementation of standard operators on the target instruction set one by one. Some native PyTorch operators could not be directly mapped to the hardware instructions, requiring hand-written kernels or semi-automated operator generation tools.

• Graph optimization and fusion: The computational graph of a three-trillion-parameter model contains a massive number of memory read/write operations. For example, fusing RMSNorm and the subsequent linear layer into a single kernel eliminates one full round-trip to memory. For a model whose memory footprint spans tens of gigabytes, this yields substantial performance gains.

• Remapping communication primitives: Under distributed parallel strategies, collective communication operations such as AllReduce, AllGather, and ReduceScatter consume a significant portion of time during training and inference. Domestic compute platforms need to optimize these primitives for their own high-speed interconnect topologies rather than relying on implementations originally designed for CPU-based communication libraries.

2.2 Ensuring Stability on the Critical Inference Path

The model has demonstrated strong results on multiple code generation and agent benchmarks, including a score of 65.7% on a terminal-operation benchmark and 61.2% on a long-horizon coding benchmark. Inference path stability is foundational to these measured results.

In practice, the development team adopted a staged validation strategy. First, they verified the numerical correctness of fundamental operator outputs on a single device, ensuring floating-point errors remained within acceptable bounds. Then they validated tensor-parallel sharding correctness in two-device and four-device environments. Finally, they verified end-to-end inference latency in multi-node environments combining pipeline parallelism and expert parallelism. The overall adaptation workflow resembled a CI/CD pipeline in software engineering, requiring regression test coverage of no less than 95% at each layer of changes.

3. Targeted Optimization for MoE Architectures and Long Contexts

Given the model’s architectural characteristics, inference optimization faces two particularly difficult problems: expert parallelism and load balancing under the MoE architecture, and memory management and scheduling for extremely long sequences.

3.1 MoE Expert Parallelism and Token Routing Strategies

In a three-trillion-parameter model, the overwhelming majority of parameters reside in multiple expert modules within the Feed-Forward Network (FFN) layers. During inference, each token does not activate all experts. Instead, a gating network dynamically selects Top-K experts. This dynamic behavior introduces two problems:

Problem 1: Uneven expert hotspots. Some high-frequency tokens, such as common English words or code keywords, are continuously routed to the same set of experts, turning the devices hosting those experts into performance bottlenecks. One response is to adopt an online load-balancing scheduler that tracks the activation frequency of each expert after every iteration and dynamically adjusts token routing weights accordingly.

Problem 2: Cross-device communication overhead. When a token is routed to an expert located on a different device, activations must be transferred over the high-speed interconnect. To reduce communication frequency, a locality-first strategy is commonly adopted: tokens are preferentially routed to local expert replicas on the current device. If the number of experts allows, a full-replication strategy can be employed, keeping a complete copy of all expert parameters on every device and eliminating cross-device expert communication entirely. However, this approach multiplies memory pressure and must be weighed carefully against available capacity.

3.2 KV Cache Management for Long Contexts

For a context window of one million tokens, unoptimized KV Cache memory usage can easily exceed 100 GB. In practice, the following three layers of optimization are commonly applied:

• KV Cache quantization: Reducing the precision of Key and Value tensors in the KV Cache from FP16 to INT8, or applying group-wise quantization, can cut memory usage by roughly 50% while preserving model accuracy.

• PagedAttention: Borrowing the virtual memory paging concept from operating systems, KV Cache is divided into fixed-size physical blocks mapped through a block table. This eliminates memory fragmentation and dramatically improves memory utilization.

• Sliding windows and key token retention: For extremely long contexts, not all historical tokens contribute equally to the current generation step. Through attention distillation or key-token filtering mechanisms, only the most recent N tokens and a small number of highly relevant historical tokens are retained, thereby compressing the physical capacity of the KV Cache.

4. From Adaptation to Production-Scale Deployment

Day-0 adaptation is only the first step. For enterprise users, the more pressing question is how to build a highly available production-grade inference service on top of such a model. Extending the perspective from this adaptation effort, several directions merit attention:

First, balancing inference throughput and latency. In long-context scenarios, the optimization targets for time-to-first-token and per-token generation latency are in tension. Time-to-first-token requires completing the prefill phase as quickly as possible, while the generation phase prioritizes throughput. In production, continuous batching is recommended to interleave the prefill and decode phases of different requests, significantly improving accelerator utilization.

Second, topology-aware scheduling for multi-device interconnects. In large-scale clusters, communication bandwidth between compute nodes is not uniform. The scheduling system needs to be aware of the physical network topology and place model shards that exchange intermediate results frequently within the same switch or rack, reducing east-west traffic across core switches.

Third, ecosystem compatibility in domestic substitution. During adaptation, it is important to maintain interface compatibility with mainstream frameworks such as PyTorch to minimize developer migration costs. At the same time, establishing a standard benchmark suite is recommended, with automatic regression tests run after every software stack upgrade to prevent performance regressions.

5. Conclusion

From hardware instruction sets to communication libraries, from operator fusion to memory management, adapting LLMs to domestic accelerators is a full-stack systems engineering effort. The successful deployment of a three-trillion-parameter model validates the feasibility and potential of domestic AI infrastructure for extremely large-scale inference scenarios. The competition in large models has shifted from a race on parameter count alone to a contest of engineering deployment capability. The ability to efficiently deploy frontier models on self-controlled computing infrastructure will become a cornerstone of competitive advantage in the AI era. For developers, a deep understanding of how model architecture characteristics align with hardware compute bottlenecks is far more meaningful than blindly chasing peak hardware performance.