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

As large language models enter the trillion-parameter era, architectures supporting ultra-long context and agent capabilities are becoming the industry standard. However, the massive parameter scale and innovative underlying mechanisms place extremely high demands on the AI infrastructure software stack. This article uses a 3-trillion-parameter model with 1-million-token context support as a case study, analyzing how to achieve day-0 seamless deployment of next-generation architecture models on domestic AI accelerator cards from three dimensions: sparse attention operator reconstruction, online MXFP4 weight conversion, and full-stack software coordination.

1. Core Challenges of Trillion-Scale Model Inference

The model discussed in this adaptation has 3 trillion parameters and introduces both sparse attention mechanisms and the MXFP4 (Microscaling FP4) quantization format. Converting it into an inference service presents two major engineering bottlenecks:

Low-level adaptation challenges for sparse attention: Traditional Transformers face quadratic growth in memory usage and computation when processing millions of tokens. This model adopts dynamic sparse attention (similar to DSA-class mechanisms), which changes the conventional token index selection and computation paths, requiring the underlying software stack to redesign attention operators and KV cache layouts.

Computational efficiency issues with low-precision weights: The Mixture-of-Experts (MoE) architecture combined with MXFP4 format can significantly compress weight storage, but hardware compute units typically cannot directly execute FP4 matrix operations. Offline dequantization to FP16 is not only extremely time-consuming but also creates dual pressure on storage and memory bandwidth.

To address these challenges, the adaptation team rebuilt the entire pipeline — from compiler and operator library to inference engine — based on an in-house full-stack software system.

2. Adapting Sparse Attention: Operator Reconstruction and Execution Pipeline Integration

Sparse attention is key to breaking through long-context limitations, but its dynamic indexing and non-contiguous memory access patterns place stringent requirements on low-level hardware operators.

2.1 Index Selection and Dynamic KV Cache Blocking

In conventional dense attention, KV cache uses contiguous physical memory allocation. In sparse attention scenarios, the model must dynamically select KV blocks for computation based on the current token. The adaptation team implemented the following optimizations at the software stack level:

• Dynamic KV cache blocking: The KV cache was restructured into a non-contiguous block-managed layout, with metadata tracking the logical position and lifecycle of each block, enabling flexible runtime pruning and merging.
• Separated prefill and decode paths: The prefill phase is compute-intensive, focusing on optimizing parallel matrix tiling strategies. The decode phase is memory-bound, focusing on optimizing sparse index merging and request batch scheduling.

Note: Block size and index format should be tuned according to hardware cache line and page table granularity. Starting with 256/512 token granularity for benchmarking is recommended.

2.2 Fused Operators and Cross-Layer State Management

A standalone sparse attention operator must be integrated into the complete execution pipeline to be effective. The team integrated customized sparse attention operators into the low-level operator library while coordinating the following logic:

• State pointer passing mechanism: For the model’s unique inter-layer state management logic, seamless state pointer passing and updating was implemented at the operator level to avoid redundant context loading.
• Multimodal cross-layer fusion: The model contains a multimodal encoder and a text decoder, with residual connections and attention computation between them requiring shared memory context. Through operator fusion, cross-layer residual computation and visual attention were merged into a single execution unit, reducing intermediate memory read/write overhead.

Risk note: If the visual encoder references third-party pretrained components, confirm copyright and distribution authorization, or use the generic term “multimodal encoder” in documentation to avoid citation risks.

3. MXFP4 Online Conversion: Real-Time Dequantization and Cross-Layer Reuse

Loading 3 trillion parameters in FP16 precision would exceed single-card memory capacity. The model stores expert weights in MXFP4 format, but hardware cannot directly execute FP4 operations, making online conversion a core breakthrough.

3.1 Real-Time Online Conversion Replacing Offline Processing

Traditional offline conversion requires dequantizing all FP4 weights to FP8/FP16 and persisting them before service startup, which is time-consuming and consumes double the storage. The adaptation team introduced an online conversion mechanism:

• Upon expert routing hits, the corresponding MXFP4 weight blocks are read from device memory.
• FP4-to-FP8 dequantization is completed at the shared memory or register level (rather than SRAM/L2 cache level, to match the actual pipeline of the target hardware).
• Results are fed directly into Tensor Cores for matrix multiplication.

Security note: FP4 weight files should be encrypted at rest, and the online conversion process should be protected against side-channel leakage. If the target hardware does not support native FP4 decoding, an FP8 offline conversion fallback can be used.

3.2 Cross-Layer Workspace Reuse for Memory Control

In MoE architectures with numerous experts, frequent allocation and deallocation of FP8 temporary workspaces causes memory fragmentation. The software stack adopts a global shared FP8 workspace strategy: a single global workspace is pre-allocated, and different Transformer layers reuse this region through memory offsets. After computation completes, only results are retained while intermediate weights are not preserved. With reasonable expert parallelism, memory growth is controllable, reducing usage by approximately 40%–60% compared to offline conversion (depending on batch size and activated expert count).

Below is simplified pseudocode for online conversion with sparse computation, with each step annotated to indicate the corresponding hardware operation layer:

# Prerequisites: routing_indices is an int32 tensor, shape=[batch, top_k];
# fp4_weights is the MXFP4-format tensor in device memory, stored in blocks by expert ID;
# kv_cache is the sparse block-managed cache object.
def moe_expert_forward(fp4_weights, routing_indices, kv_cache):
    # [Step 1] Routing index selection -- scheduler/CPU layer
    selected_experts = sparse_router(routing_indices)
    # [Step 2] Acquire global shared FP8 workspace -- memory allocator layer
    fp8_workspace = get_global_fp8_workspace()
    for expert_id in selected_experts:
        # [Step 3] Online conversion: MXFP4 -> FP8 at shared memory/register level
        fp8_weight = online_dequantize_mxfp4_to_fp8(
            fp4_weights[expert_id],
            target_buffer=fp8_workspace
        )
        # [Step 4] Sparse attention computation -- GPU core/Tensor Core layer
        attn_output = sparse_attention_op(
            query=current_query,
            kv_cache=kv_cache.get_sparse_blocks(expert_id),
            mode="decode"
        )
        # [Step 5] Expert FFN computation -- Tensor Core/matrix unit layer
        expert_output = fp8_matmul(attn_output, fp8_weight)
    # Workspace is left for the next layer to reuse; no explicit release needed
    return aggregate_outputs(expert_outputs)

4. Full-Stack Software Coordination

The optimizations described above depend on end-to-end coordination from compiler to operator library. Key components in this adaptation included:

1. High-performance operator library: Provides foundational computation and customized sparse attention operator implementations, supporting non-contiguous memory access patterns.
2. Intermediate representation and compiler: Compiles the inference engine computation graph into hardware-executable instructions, optimizing register allocation and instruction pipelining.
3. DSL compilation framework: Introduces domain-specific languages similar to Triton and TileLang with domestic GPU backends, enabling developers to write custom operators in high-level languages with automatic tiling and parallelism optimization.

5. Results and Engineering Takeaways

Through full-stack software optimization, the team successfully established the complete pipeline for model loading, service startup, and basic text/image inference. In the adaptation environment (domestic AI accelerator card, batch_size=1, FP8 inference), time-to-first-token decreased by approximately 30% compared to the offline conversion approach, with peak memory usage controlled within single-card capacity.

Best practice checklist:

• Confirm sparse attention index format aligns with hardware page table
• Evaluate time/memory tradeoffs between online and offline conversion
• Enable encrypted storage for FP4 weight files
• Verify copyright compliance of multimodal components
• Complete block-size granularity benchmarking on target hardware

This work demonstrates that for ultra-large-scale parameter models, relying solely on hardware compute scaling is insufficient. Breakthroughs must come from software-hardware co-design. Whether through sparse attention operator reconstruction or online MXFP4 weight conversion, the flexibility of the underlying software stack is critical infrastructure for large model deployment. As model architectures continue to evolve, full-stack co-optimization capability will become a core competitive advantage for AI computing platforms.