Domestic GPU Achieves Day-0 Adaptation for 3-Trillion-Parameter Model, Enabling Text and Image Inference

Background: The Extreme Infrastructure Challenge of 3-Trillion-Parameter Models

Large model parameter counts are growing exponentially, moving from hundreds of billions into the trillion and even 3-trillion range. This leap in scale not only promises stronger intelligence but also imposes unprecedented demands on the underlying AI compute platform:

• Memory capacity bottleneck: Even with low-precision quantization, a 3-trillion-parameter model’s weights reach terabyte scale, far exceeding single-card memory limits. Multi-card distributed inference and efficient memory reuse strategies become mandatory.
• Heterogeneous computation patterns: Such models commonly adopt hybrid attention mechanisms, combining standard Full Attention with Sparse Attention to reduce compute overhead for long-sequence inference. Sparse attention involves complex computation paths such as index selection and KV cache layout optimization, directly testing the software stack’s operator coverage.
• Long-context inference challenge: A context window of one million tokens means KV Cache memory usage grows linearly with sequence length. Maintaining stable inference through paged management, KV compression, and multi-level offloading is a core engineering challenge.
• Multimodal inference requirements: Beyond pure text, the model must support image input. Feature alignment between the vision encoder and the language model backbone, along with cross-modal attention computation, requires the inference framework to have flexible pre- and post-processing extension capabilities.

Against this backdrop, whether and how a domestic compute platform can efficiently handle Day-0 adaptation (completing deployment readiness on the model’s release day) for ultra-large-scale models has become a key benchmark for AI infrastructure maturity.

Core Approach: A Software-Hardware Co-Designed “Layered Adaptation” Methodology

Achieving Day-0 adaptation is far from simply loading and forwarding model files. It is a complete systems engineering effort spanning the framework layer, operator layer, runtime layer, and communication layer. The overall approach can be broken down into four key stages.

1. Proactive Model Architecture Analysis and Compute Feature Mapping

Before obtaining model weights, a static analysis of the model’s operator composition must be performed based on public technical reports and architecture descriptions. A 3-trillion-parameter model typically features deep layer counts (hundreds of Transformer blocks), wide hidden dimensions (tens of thousands), and hybrid attention branches.

At the operational level, key computation paths must be identified:

• Standard attention path (QKV projection, dot-product attention, output projection);
• Sparse attention path (index partitioning, Top-k selection, block-sparse matrix multiplication);
• Vision encoding path (image patch embedding, vision Transformer stacking);
• Residual and normalization path (cross-layer residual connections, LayerNorm/RMSNorm).

Through architecture decomposition, it becomes possible to identify in advance which operators can reuse existing high-performance libraries and which require new implementations. This is the first step of Day-0 adaptation and a critical predictor of subsequent workload.

2. Framework Compatibility Layer: Reusing Upstream Model Implementations to Reduce Migration Cost

Current mainstream inference frameworks are written in Python and interact with low-level operator libraries through unified scheduling interfaces. The shortcut to rapid adaptation is not reinventing the wheel but reusing authoritative upstream model implementations and “bridging” them to the target hardware through the software stack’s compatibility interfaces.

Specifically, the software stack must provide compatibility at three levels:

• Programming model layer: Support CUDA-like parallel programming paradigms so that upstream operator kernels can be compiled to the target GPU architecture without modification;
• Runtime interface layer: Provide semantically equivalent memory management, stream synchronization, and event notification interfaces, ensuring the framework’s asynchronous execution logic maps correctly;
• AI framework layer: Interface with mainstream deep learning frameworks’ automatic differentiation and tensor abstractions, so that model code built on these frameworks can be loaded and executed directly.

Through these three compatibility layers, teams can seamlessly migrate model definitions already implemented in high-performance inference frameworks like SGLang to domestic GPUs, avoiding the enormous workload of writing model inference logic from scratch.

3. Operator Library and Compiler: A Dual-Engine Approach to Critical Computation Paths

Framework migration is only the first step. The real challenge is performance. The sparse attention operators and vision Transformer operators involved in a 3-trillion-parameter model often cannot directly use general-purpose operator libraries and require customized optimization through the following toolchain combination:

• Operator library: Provides pre-compiled high-performance dense matrix multiplication (GEMM), normalization, activation functions, and other foundational operators to ensure core compute throughput;
• Compiler: Through Triton-like domain-specific languages (DSLs) and TileLang-like scheduling languages, developers can write high-performance custom operators in Python syntax, with the compiler handling automatic vectorization, shared memory allocation, and instruction-level optimization;
• Low-level compilation toolchain: Supports the complete compilation flow from high-level DSL to low-level machine code, and provides a manual assembly-level tuning channel for hotspot operators.

Taking sparse attention as an example, its computation divides into Prefill and Decode phases. The Prefill phase must process sparse indices for the entire input sequence and compute the attention matrix; the Decode phase processes only a single new token, involving incremental KV Cache reads. The operator library must provide sparse computation kernels for both paths, maintaining a consistent memory view for index selection and KV layout.

4. Pipeline Integration of Hybrid Attention and Vision Computation

For models with both text and image inference capabilities, the entire execution chain can be viewed as a multi-stage pipeline:

[Image Input] -> [Vision Patching] -> [Vision Transformer Encoding] -> [Cross-Modal Projection]
                                                                          ↓
[Text Input] -> [Tokenization] -> [Embedding] -> [Sparse Attention Layers] -> [Full Attention Layers] -> [Residual Merge] -> [Output Layer]

The sparse attention layers and vision attention layers share the KV Cache management module but use different indexing strategies. The software stack must uniformly manage the states of both attention types and enable zero-copy transfer for cross-layer residual connections. This integration determines whether the model can handle mixed text-image input within a single service instance without being split into two independent services.

Engineering Practice: Solutions to Three Critical Problems

Problem 1: What to Do When Memory Cannot Hold 3 Trillion Parameters?

This is a physical reality that all trillion-scale model inference must confront. The solution strategy is multi-layered:

• Model parallelism: Partition the model across multiple cards by layer or tensor dimension, with each card holding only a portion of the weights;
• KV Cache paged management: Drawing on operating system virtual memory paging mechanisms, KV Cache is divided into fixed-size blocks that are logically contiguous but physically dispersed, effectively eliminating memory fragmentation;
• Dynamic weight offloading: For rarely accessed layers (such as embedding layers), weights can be temporarily stored in CPU memory and swapped into GPU memory on demand.

The software stack must provide a unified device memory management interface for these strategies, enabling the framework layer to invoke memory allocation, release, and migration operations transparently.

Problem 2: How to Prevent Sparse Attention from Slowing Down Overall Decoding Speed?

The core benefit of sparse attention is reducing computational complexity for long sequences (from O(n²) to O(n log n) or O(n)), but if index computation overhead is too high, the benefit is negated. Engineering trade-offs include:

• Parallelizing index computation with matrix multiplication: Using independent compute streams for Top-k index selection so it does not block the main compute stream;
• Leveraging the block structure of sparse matrices: Partitioning the attention matrix into fixed block sizes (e.g., 64x64 or 128x128), skipping zero blocks, and performing matrix multiplication only on non-zero blocks to effectively utilize Tensor Core dense compute;
• Optimizing contiguous KV Cache reads during Decode: Reducing memory access latency through prefetch instructions.

In practice, these optimizations can keep the actual sparse attention latency within 15% of the ideal value under the theoretical sparsity ratio, ensuring acceptable end-to-end inference latency.

Problem 3: How to Ensure Efficient Multi-Card Communication?

When a 3-trillion-parameter model runs in multi-card parallelism, every computation step involves communication operations. Communication volume directly determines scaling efficiency. Optimization directions include:

• Adopting high-bandwidth, low-latency interconnect topologies (such as NVLink-equivalent technologies) and overlapping communication with computation—prefetching weights and KV data for the next layer while the current layer computes;
• Using communication primitive libraries (such as NCCL-equivalent implementations) that provide efficient implementations of collective communication operations like All-Reduce and All-Gather, with topology-aware communication scheduling;
• Fine-grained partitioning of model parallelism dimensions so that communication occurs on the fastest path and avoids hotspot communication conflicts.

Deployment Form and Serving Practice

After model adaptation, the final deliverable is a stable, usable inference service. This requires solving several engineering problems in production environments:

• Service startup and warmup: Model weight loading uses memory-mapped (mmap) files for second-level loading. After loading, a small number of virtual requests are executed to warm up operators, avoiding timeout on the first real request;
• High-concurrency scheduling: The inference service integrates dynamic batching strategies, mixing Prefill and Decode phases of multiple requests to improve GPU utilization;
• Observability: Provides visualized monitoring of core metrics including inference latency, throughput, memory usage, and KV Cache hit rate, supporting real-time alerting and auto-scaling;
• Multimodal routing: After a request arrives, a routing layer first determines whether image input is included, then selects either the pure-text inference path or the mixed text-image path. Both share the underlying KV Cache pool to improve memory utilization.

Summary and Future Outlook

Completing Day-0 adaptation for a 3-trillion-parameter model on a domestic GPU with a self-developed software stack validates that domestic AI infrastructure is now capable of handling the industry’s most advanced large models. The core methodology can be summarized as:

1. Framework compatibility rather than rewriting, maximizing reuse of the community ecosystem;
2. Operator library and compiler co-optimization, balancing rapid adaptation with deep tuning;
3. Layered decoupling of memory and communication management, overcoming physical resource constraints;
4. Integrated service engineering design, ensuring production-grade availability.

Looking ahead, as model parameters advance toward the 10-trillion scale, inference infrastructure will face even more severe memory wall and communication wall challenges. Continuous innovation directions include more aggressive quantization compression (such as 1-bit weights), CXL (Compute Express Link)-based memory pooling expansion, and exploration of novel architectures combining sparse computing with in-memory computing. The role of domestic compute platforms in this process will not merely be that of a follower but potentially a participant in defining the next generation of inference paradigms.