Optimizing Trillion-Parameter Model Inference: A Domestic Full-Stack Architecture Approach

As large language models scale towards the trillion-parameter mark and integrate multimodal capabilities like text and image processing, the complexity of adapting underlying computing power and software stacks increases exponentially. Recently, an open-weight model series released its most powerful 3-trillion-parameter version. It supports a 1-million-token context window and achieves performance approaching top-tier closed-source models in benchmarks for code generation and complex Agent tasks. Furthermore, it introduces sparse attention and low-bit quantization technologies, posing significant challenges to hardware memory bandwidth, operator compatibility, and multi-GPU communication.

This article analyzes the technical methodology for completing Day-0 adaptation of such ultra-large parameter models based on a domestic full-stack software architecture and AI computing hardware, while establishing a foundational text and image inference pipeline.

1. Underlying Logic for Rapid Migration: High-Compatibility Software Stack Design

In the adaptation of trillion-parameter models, “Day-0 support”—meaning the model is adapted for the hardware the moment it is released—is a critical metric for evaluating software stack maturity. The core of rapid migration lies in the software stack’s high compatibility at the levels of programming models, runtime interfaces, and deep learning frameworks.

1.1 Framework Integration and Operator Library Synergy

Instead of building an inference engine from scratch, the technical team leveraged mainstream LLM inference frameworks like SGLang to承接 the upstream model implementation. This is made possible by the underlying software stack’s seamless integration with mainstream frameworks via standard interfaces. The migration and integration of key computing paths under the new architecture were accomplished using a proprietary or deeply optimized operator library (such as MATE), Triton dialect extensions (Triton-MUSA), tile programming languages, and specialized compilers.

This layered architecture design allows community-standard model code to be directly transformed into practical inference services on specific hardware. The software stack covers the entire lifecycle: “Framework Execution -> Operator Compilation -> Device Execution -> Multi-GPU Communication.” This ensures that model adaptation maximizes the reuse of mature interfaces while leaving room for targeted optimization of new computing paths.

1.2 Multi-GPU Communication and Memory Scheduling

For a 3-trillion-parameter model, the VRAM of a single GPU is far from sufficient to hold the complete weights. The software stack must support highly efficient tensor parallelism and pipeline parallelism. During the loading phase, the framework coordinates multiple AI computing cards to load weight shards via high-speed interconnect buses. During the execution phase, it handles cross-card All-Reduce and Send/Recv communications to ensure low-latency multi-GPU collaborative inference.

2. Core Technical Breakthrough: Enabling DSA Sparse Attention

To balance a 1-million-token context window with inference efficiency, the new model employs a sparse attention mechanism. Unlike traditional Multi-Head Attention (MHA) or Grouped-Query Attention (GQA), Dynamic Sparse Attention (DSA) reduces unnecessary KV cache computation through dynamic index selection, requiring deep customized adaptation of the underlying software stack.

2.1 Index Selection and KV Cache Layout Reconstruction

In the DSA mechanism, the model does not perform global attention computation on all historical tokens; instead, it filters key token indices based on specific rules. The technical team adapted the index selection logic at the operator level and reconstructed the memory layout of the KV cache. Because sparse access increases randomness, the KV cache requires a non-contiguous memory block management strategy to balance memory access hit rates and VRAM utilization.

2.2 Prefill and Decode Dual-Path Computation Optimization

LLM inference is divided into two stages: Prefill (first token generation) and Decode (autoregressive generation). In the Prefill stage, DSA operators must process large-scale parallel Token matrices, resulting in high computational density. In the Decode stage, the memory access bottleneck becomes the primary constraint. The underlying software stack optimized the computing paths for both stages using MATE sparse attention operators: the Prefill stage fully utilizes computing power to execute matrix multiplication concurrently, while the Decode stage uses operator fusion to reduce intermediate results being written back to VRAM, thereby improving overall throughput.

2.3 State Management and Cross-Layer Residual Fusion

Beyond basic attention, the complete execution chain involves KDA (Key-Value Data Abstraction) state management, cross-layer residual connections, and the fusion of visual Attention. These mechanisms require the framework to maintain strict operator dependency relationships during scheduling. By decoupling the sparse attention operators from the state management module and orchestrating them through a unified scheduler, the system achieved stable data flow of multimodal inputs across complex network structures.

3. Online Conversion and Deployment Strategy for MXFP4 Expert Weights

Under the Mixture of Experts (MoE) architecture, the expert weights of a 3-trillion-parameter model are exceptionally massive. To reduce VRAM consumption, the model uses the MXFP4 (Microscaling FP4) data format for weight quantization. However, executing FP4 computations directly on hardware may face precision loss or computing power mismatches, necessitating a clever conversion strategy.

3.1 Abandoning Offline Conversion: The Necessity of Online Conversion

Traditional offline conversion schemes require generating full FP8 or FP16 weights before deployment. For a 3-trillion-parameter model, this means requiring several terabytes of additional storage space and lengthy conversion times, severely contradicting the rapid deployment principle of Day-0 support. To address this, the technical team innovatively introduced an online conversion mechanism.

3.2 Cross-Layer Reuse of FP8 Workspace

Before expert computation execution, the system does not pre-generate full offline FP8 weights; instead, it decodes MXFP4 weights into FP8 format in real-time within the computation stream. To control the VRAM overhead introduced by online conversion, the team adopted an engineering strategy of “cross-layer reuse of the FP8 workspace.” Specifically, the system pre-allocates a fixed-size FP8 Workspace in VRAM. Once an expert layer completes its computation, this workspace is immediately reused by the next expert layer. This “on-the-fly conversion, circular reuse” mechanism not only prevents exponential VRAM expansion but also hides conversion latency through streaming computation, offering a new paradigm for the lightweight and rapid deployment of ultra-large models.

4. Engineering Implementation of the Multimodal Inference Pipeline

Establishing a mixed inference pipeline for text and images is another core objective of this adaptation. Multimodal inference requires the model to process text tokens while also encoding and projecting image features.

4.1 Bridging Visual Computing and Language Models

Visual feature extraction typically relies on an independent Vision Transformer structure. The underlying software stack integrates visual Attention operators to deeply fuse the image feature extraction process with the language model’s Decoder layer computation. During the cross-modal alignment phase, the image features are mapped through a projection layer and concatenated with the embeddings of text Tokens to jointly serve as the input for the large language model.

4.2 Service-Oriented Launch and End-to-End Verification

After completing the adaptation of underlying operators and frameworks, the team verified model loading, service startup, and the foundational inference pipeline. Taking a tens-of-billions-parameter visual multimodal model as an example, its service-oriented encapsulation generally follows these engineering practices:

# Pseudo-code illustration: Multimodal model service startup and inference logic
import inference_engine as ie
# Initialize multi-GPU environment and distributed inference engine
engine = ie.DistributedEngine(
    devices=[0, 1, 2, 3],
    parallel_strategy="tensor_parallel"
)
# Load MXFP4 MoE model weights (supports online conversion)
model = engine.load_model(
    model_path="path/to/trillion_param_model",
    dtype="mxfp4",
    online_quant_conversion=True,
    kv_cache_layout="sparse_dsa"
)
# Start multimodal inference service
server = ie.InferenceServer(model=model, port=8080)
server.start()
# Simulate multimodal input request
request = {
    "text": "Analyze the data trend of this chart",
    "image_url": "path/to/image.png",
    "max_tokens": 2048
}
response = server.generate(request)
print(response)

5. Conclusion and Future Outlook

This Day-0 adaptation practice for the 3-trillion-parameter multimodal large model not only validates the feasibility and efficiency of the domestic full-stack software and hardware architecture when handling ultra-large-scale models but also establishes a standardized adaptation methodology. This includes achieving rapid framework integration through a highly compatible software stack, overcoming sparse attention challenges through customized operators, and resolving VRAM bottlenecks via online conversion and workspace reuse.

As LLMs evolve towards Agentic workflows and long-range contexts, the collaborative optimization of underlying computing power and software stacks will become the decisive factor in model deployment efficiency. Moving forward, with the further enrichment of operator libraries and deeper compilation optimizations, trillion-parameter model inference on large-scale GPU clusters will become increasingly accessible and efficient. When building such LLM applications, enterprises should focus heavily on the full-stack collaboration capabilities of underlying software and hardware to secure an advantage in the wave of artificial intelligence.