Day-0 Adaptation for Trillion-Parameter LLMs: Sparse Attention and MXFP4 Inference Optimization Practices

Day-0 Adaptation for Trillion-Parameter LLMs: Hardware-Software Co-design Breakthroughs

As large language model parameters scale toward the trillion level, universally integrating ultra-long context and multimodal capabilities, underlying hardware architectures and software stacks face unprecedented inference pressures. Recently, a team announced the successful Day-0 adaptation of the initial release of a 3-trillion-parameter model based on their self-developed AI training and inference accelerator cards and underlying software stack. This fully enabled model loading, service startup, and basic text and image inference pipelines. This practice not only validates the feasibility of heterogeneous computing hardware supporting top-tier open-source models but also establishes a general optimization methodology for ultra-large model inference.

I. Core Pain Points and Technical Challenges of Ultra-Large Model Inference

Before discussing the adaptation approach, it is necessary to break down the three core challenges that trillion-parameter models bring to actual deployment:

1. Memory Wall and Loading Bottlenecks: Even with 4-bit quantization, the weight volume of a 3-trillion-parameter model remains massive. How to achieve efficient loading within limited memory bandwidth and capacity determines the time cost of service startup.
2. Attention Overhead of Long Context: Supporting million-level context means KV cache will grow linearly or even quadratically with sequence length. Traditional dense attention mechanisms will exhaust memory in this scenario, necessitating long-text optimization solutions such as sparse attention.
3. Computation Fragmentation from Multimodality and Mixed Precision: When a model processes text and images simultaneously and uses low-precision (e.g., MXFP4) expert weights, the computation graph becomes extremely complex. If tensor operations of different precisions and modalities require frequent context switching or memory transfers, computing utilization will plummet.

Addressing these challenges, the adaptation team did not stop at merely “getting it to run” but delved into computation graph reconstruction and operator-level optimization. This is the core value of Day-0 adaptation.

II. Inference Pipeline Reconstruction Based on the SGLang Architecture

To support the massive codebase of the upstream open-source model, the adaptation work first selected a high-performance inference framework as a bridge and performed targeted modifications to its underlying instruction sets and software stack.

1. Cross-Architecture Compiler and Operator Library Integration

Through a self-developed operator library, Triton dialect support, the TileLang operator development stack, and an underlying compiler, the team completed the migration of key computation paths for the new architecture. The core of this process lies in transforming general community model code into an actual inference service on specific hardware. The technical pipeline can be abstracted as follows:

# Pseudo-code illustration: Operator integration and compilation process
def load_model_to_hardware(model_path):
    # 1. Parse the model computation graph via the general operator library
    graph = parse_computation_graph(model_path)
    # 2. Operator compilation and instruction set mapping
    for node in graph.nodes:
        if node.type == "ATTENTION":
            # Map to hardware-specific sparse attention operator
            node.kernel = compile_to_mate_sparse_attn(node.attrs)
        elif node.type == "MXFP4_LINEAR":
            # Map to mixed-precision computing unit supporting FP8 workspace
            node.kernel = compile_to_fused_mxfp4_fp8(node.attrs)
    # 3. Generate hardware executable
    return hardware_executable(graph)

In this process, the cross-layer compiler plays a crucial role. It is responsible not only for translating high-level operators to underlying assembly but also for hardware-level optimizations such as register allocation and shared memory reuse. This ensures that community code can be smoothly migrated to the target hardware platform without large-scale rewrites.

2. Model Loading and Service Startup Acceleration

Faced with massive weight files, the system leveraged the collaboration between a container platform and high-speed object storage to achieve parallel chunked loading of weight files. By optimizing the PCIe data transfer bandwidth utilization from the host to the accelerator cards and combining it with Tensor Parallelism (TP) strategies, the loading process—which could originally take hours—was compressed to the minute level, meeting the requirements for rapid Day-0 service deployment.

III. Deep Optimization: Comprehensive Adaptation of Sparse Attention DSA

The target model employs an advanced sparse attention mechanism (DSA), which is key to achieving million-level context inference. However, the execution efficiency of DSA on underlying hardware highly depends on index calculation and cache layout.

1. Index Selection and KV Cache Layout Optimization

In the Prefill and Decode phases, the attention computation patterns are drastically different. The adaptation team redesigned the index selection algorithm based on hardware characteristics. In the Prefill phase, the system favors reading large contiguous memory blocks; therefore, by reorganizing the KV cache layout, it more closely aligns with the hardware’s large-granularity memory access characteristics in physical memory.

In the Decode phase, since only one token is generated at a time, the memory access bottleneck becomes prominent. By introducing a KDA (Key-Data-Array) state management mechanism, the team converted historical KV cache query operations into efficient hardware parallel table lookups, significantly reducing decoding latency.

2. Cross-Layer Residual and Visual Attention Fusion

The integration of multimodal capabilities often brings complex cross-layer computation dependencies. When processing images, the output of visual attention requires cross-layer residual fusion with text features. In the adaptation scheme, this part of the operation was deeply fused into the underlying implementation of the MATE operator library. Through operator fusion technology, activation functions, matrix multiplications, and residual additions—originally requiring multiple memory accesses—were merged into a single compute kernel. This significantly reduced intermediate results writing back to memory and improved the overall throughput of multimodal inference.

IV. Dynamic Conversion and Computation Reuse of MXFP4 Expert Weights

Mixture of Experts (MoE) models have become standard at the trillion-parameter scale, and lower precision quantization (such as MXFP4) is a sharp tool for reducing memory usage. However, not all hardware can natively and efficiently execute 4-bit floating-point computations.

1. Online Conversion Strategy

Faced with the model’s MXFP4 expert weights, the traditional approach is to convert them to hardware-supported formats (such as FP8) and store them in advance. However, this multiplies storage costs and loading times. The adaptation team innovatively adopted an “online conversion and cross-layer FP8 workspace reuse” strategy.

Specifically, when an expert is activated, the system dequantizes the MXFP4 weights to FP8 format in real-time before computation and places them into a pre-allocated FP8 workspace for matrix multiplication. Although this process adds a small amount of computational overhead, it completely eliminates the step of generating massive offline FP8 weights, returning the bulk of memory usage to the model itself.

2. Cross-Layer Workspace Reuse Mechanism

To further control the memory watermark, the team designed a cross-layer workspace reuse mechanism. Since experts in different layers of an MoE model are typically not fully activated at the same time, the system designed the FP8 workspace as a shared memory pool. Different Transformer layers can reuse the same physical memory region when executing expert computations.

This mechanism not only avoids excessive memory pre-allocation but also reduces the risk of memory fragmentation, making concurrent inference of a 3-trillion-parameter model possible under limited hardware resources.

V. Industry Insights and Future Outlook for Heterogeneous Computing Adaptation

This Day-0 adaptation for a trillion-parameter model is not just a victory for a single hardware-software stack; it provides a highly valuable practical path for the entire large model inference ecosystem. Whether for general cloud service providers like Baidu AI Cloud or independent computing hardware providers, the following experiences are particularly crucial:

1. Deep Hardware-Software Co-design is the Breakthrough Point: Relying solely on hardware computing power stacking cannot solve the complex problems of large model inference. Deep collaboration through self-developed operator libraries, underlying compilers, and upper-level frameworks is necessary to squeeze every ounce of performance from the hardware.
2. Dynamic Memory Management Will Become Standard: Faced with increasingly large models and longer contexts, fine-grained memory management technologies such as online precision conversion and cross-layer workspace reuse are necessary means to break through the “memory wall.”
3. Ecosystem Compatibility Determines Deployment Speed: Compatibility with mainstream open-source community code and achieving transparent acceleration through compiler-layer technologies can minimize developer migration costs, which is the foundation for achieving Day-0 adaptation.

In the future, with the popularization of AI Agents and long-horizon task orchestration, underlying computing power will face more complex call graphs and stricter latency requirements. Continuously deepening operator fusion and exploring next-generation sparsification and low-precision computing architectures will be the long-term direction for heterogeneous computing software stack evolution. Relying on mature container platforms, monitoring and alerting, and log service systems, enterprises will be able to build stable and efficient large model inference foundations more calmly, welcoming the comprehensive arrival of the artificial general intelligence era.