Domestic Full-Stack GPU Adapts Trillion-Parameter Model Inference

1. Background: New Inference Demands from Trillion-Parameter Models

Large language models have continued to grow rapidly in scale, with parameter counts moving from hundreds of billions to trillions. A recently open-sourced frontier model released a 3-trillion-parameter version that also supports a 1-million-token context window and multimodal inference across text and images. Deploying and serving models of this scale places extreme demands on the underlying compute hardware and software stack. Beyond high-bandwidth memory and massive parallel computing capability, effective support requires coordinated optimization across drivers, compiler runtimes, and upper-level inference frameworks.

Domestic full-stack GPUs are playing an increasingly important role in this wave of large model development. GPU accelerators built around a unified training-inference architecture have demonstrated strong engineering capability through Day-0 adaptation, meaning the basic inference pipeline is brought up on the same day a model is released. This article uses that adaptation effort as a starting point to examine the technical logic behind supporting trillion-parameter model inference on domestic GPUs, covering architecture design, software stack coordination, and key stages of the inference pipeline.

2. Core Architecture: The Design Philosophy of Unified Training-Inference GPUs

2.1 Why Emphasize Unified Training and Inference

Traditional GPUs often treat training and inference as separate workloads with distinct hardware resource scheduling strategies. In contrast, a unified training-inference architecture aims to serve both high-throughput training and low-latency inference on the same accelerator. By using shared compute units, memory management, and scheduling mechanisms, it reduces data movement overhead and improves overall resource utilization.

For trillion-parameter models, inference-time memory usage remains substantial. Under FP16 precision, the weights of a 3-trillion-parameter model alone require roughly 600 GB of memory. Adding KV cache, activations, and other runtime buffers places even higher demands on per-card memory capacity and multi-card interconnect bandwidth. Unified training-inference architectures typically feature large high-bandwidth memory and high-speed inter-card connectivity, providing a solid hardware foundation for large model inference.

2.2 Memory Management and Model Parallelism Strategies

When a single card cannot hold the entire model, engineering practice usually employs several parallelism strategies:

• Tensor Parallelism: Splits the weights of each layer across multiple cards. Each card computes part of the matrix operations, and results are combined through AllReduce.
• Pipeline Parallelism: Divides the model by layers, with different cards responsible for different layers. Micro-batch scheduling enables pipelined execution.
• Expert Parallelism: For Mixture of Experts (MoE) architectures, different experts are assigned to different cards. Only a subset of experts is activated, reducing total computation.

The memory controller and interconnect topology of a domestic full-stack GPU must support these parallelism modes simultaneously, which places high demands on the scheduling capability of the driver and runtime layers.

3. Software Stack Coordination: From Driver to Inference Framework

3.1 The Critical Role of an In-House Software Stack

The competitiveness of domestic GPUs depends not only on hardware specifications but also on how efficiently their in-house software stacks expose hardware capabilities to upper-level applications. A general-purpose GPU programming framework, for example, needs to provide abstraction levels comparable to mainstream GPU programming models, ranging from low-level memory management and thread scheduling to mid-level operator libraries and compiler optimizations, and up to graph compilers and runtimes.

In the adaptation of the trillion-parameter model, the core tasks of the software stack include:

1. Operator adaptation and optimization: Mapping core operators such as Attention, LayerNorm, and FFN onto the domestic GPU compute units efficiently, with instruction-level optimization tailored to hardware characteristics.
2. Model loading and weight conversion: Loading weights from common model formats such as Safetensors and PyTorch Bin, and performing precision conversion and layout adaptation.
3. Memory planning and scheduling: Allocating model weights, KV cache, and temporary buffers within limited memory to avoid out-of-memory failures.
4. Service startup and API integration: Packaging inference capability as standard API services supporting protocols such as HTTP and gRPC for easier integration with upper-level business systems.

3.2 The Engineering Significance of Day-0 Adaptation

Day-0 adaptation refers to completing basic inference pipeline validation on the same day a model is officially released. Achieving this requires a highly automated adaptation pipeline:

• Automated model parsing: Automatically parsing the model graph and generating an operator call sequence.
• Pre-release compatibility validation: Running preliminary tests on existing models with similar architectures before the new model is released to identify potential missing operators or precision issues in advance.
• Rapid iteration loop: When issues are identified, driver, compiler, and operator library teams collaborate on fixes and complete updated validation within hours.

This rapid-response capability is an important sign of a maturing domestic GPU ecosystem.

4. Inference Pipeline Details: Text and Multimodal Inference

4.1 Text Inference Pipeline

Text inference is the most fundamental capability of large models. For a trillion-parameter model, the complete inference flow is as follows:

[User input] → [Tokenizer encoding] → [Embedding lookup] → [Transformer layer-by-layer computation] → [Logits output] → [Sampling/decoding] → [Detokenizer] → [Response output]

The layer-by-layer computation of Transformer blocks consumes the majority of compute resources. Each Transformer layer typically includes:

• Self-Attention: Computes Q, K, and V matrices, performs attention weight calculation and weighted summation.
• FFN (Feed-Forward Network): Two linear transformations with an activation function in between.
• Residual connections and LayerNorm: Stabilize numerical precision during training and inference.

Implementing efficient Attention computation on domestic GPUs usually requires deep optimization targeting hardware matrix multiplication units such as Tensor Core-class units, including tiled computation, instruction pipeline scheduling, and memory prefetch strategies.

For ultra-long contexts of up to 1 million tokens, KV cache management becomes especially critical. During inference, the Key and Value vectors of all historical tokens must be cached, causing memory usage to grow linearly with sequence length. Common mitigation strategies include:

• KV cache quantization: Compressing KV cache from FP16 to INT8 or even INT4, trading a small precision loss for several times the memory savings.
• Paged Attention: Borrowing the paging concept from operating system virtual memory, the KV cache is divided into fixed-size blocks allocated on demand, reducing fragmentation waste.
• Sliding window or sparse attention: Approximating computation for distant tokens to reduce complexity.

4.2 Image Inference Pipeline

Multimodal capability is a key direction for current large models. Image inference typically involves joint reasoning between a vision encoder and the language model:

1. Image preprocessing: Resizing, cropping, and normalizing the input image to the expected resolution of the model.
2. Visual feature extraction: Converting the image into a sequence of visual tokens through a vision encoder such as a ViT-based architecture.
3. Cross-modal fusion: Concatenating visual tokens with text tokens before feeding them into the language model for joint inference.
4. Result generation: The model outputs a text description or a structured answer.

Achieving efficient vision encoder inference on domestic GPUs requires particular attention to convolution and image transformation operator optimization. In addition, since the number of visual tokens is usually far greater than text tokens, with a single image potentially generating hundreds to thousands of visual tokens, both memory pressure and computational load increase significantly, placing higher demands on parallelism strategy selection and batch scheduling.

5. Engineering Challenges and Responses

5.1 Precision Consistency

Different GPU architectures implement floating-point operations with subtle differences. Under the deep accumulation of a trillion-parameter model, these differences may cause inference results to deviate from reference platforms. Solutions include:

• Applying strict numerical validation in critical operators to ensure errors remain within an acceptable range.
• Providing precision comparison tools and automated regression testing.

5.2 Memory Fragmentation and Resource Scheduling

Large model inference is a memory-intensive task. Fragmentation can seriously degrade the efficiency of multi-user concurrent services. Recommended practices include:

• A unified memory pool management mechanism that pre-allocates large memory blocks and subdivides them on demand.
• Dynamic batching that flexibly adjusts the number of concurrent requests based on available memory.

5.3 Monitoring and Failure Recovery

Inference services for trillion-parameter models must run reliably around the clock, requiring a comprehensive monitoring system:

• Real-time collection of hardware metrics such as memory utilization, temperature, and power consumption.
• Continuous monitoring of service metrics such as inference latency, throughput, and error rate.
• Automatic restart and hot-switch mechanisms to ensure high service availability.

6. Ecosystem Outlook and Selection Considerations

The rapid progress of domestic full-stack GPUs in large model inference provides enterprises with more diverse compute options. When evaluating hardware for actual deployment, the following dimensions deserve attention:

1. Software stack maturity: Operator library coverage, compiler optimization quality, and compatibility with mainstream inference frameworks.
2. Ecosystem compatibility: Support for mainstream model formats and the availability of migration toolchains.
3. Scalability for large-scale deployment: Multi-card interconnect performance, cluster scheduling efficiency, and service-oriented deployment solutions.
4. Long-term support and iteration speed: Collaboration efficiency between hardware and software teams, and adaptation speed for new models.

Cloud platforms are also continuing to build heterogeneous compute resource pools to provide enterprises with flexible compute scheduling and model deployment services. Users can combine containerized deployment, elastic scaling, and other cloud capabilities to build efficient large model inference infrastructure according to their business needs.

7. Conclusion

Day-0 adaptation of a trillion-parameter model is not only a technical validation but also a concentrated demonstration of the full-stack coordination capability of domestic GPU hardware and software. From the unified training-inference hardware architecture to operator optimization in an in-house software stack, and through the complete text and multimodal inference pipeline, every stage requires close collaboration among hardware, driver, compiler, framework, and application layers. As the domestic GPU ecosystem continues to mature, the path toward locally controlled large model inference will become increasingly clear, offering developers and enterprise users more cost-effective and autonomous technology choices.