System-Level Optimization and Full-Stack Adaptation for Trillion-Scale Sparse MoE Multimodal Models

In recent years, as large model parameter scales push toward the trillion level, native multimodal capabilities and long-context lengths have become core industry trends. A typical representative is the sparse Mixture-of-Experts (MoE) model with a total parameter count reaching 2.8 trillion (approximately 3 trillion). These models usually feature hundreds to thousands of routing experts, activating only a tiny fraction of them per token computation. However, training and fine-tuning such models on limited hardware clusters tests the limits of underlying compute throughput and presents unprecedented challenges in mixed parallelism strategies, memory management, and operator customization. This article decouples specific product attributes to deeply explore the system-level adaptation and optimization practices for ultra-large sparse MoE multimodal models from a general technical perspective.

1. Core Architectural Features of Ultra-Large MoE Multimodal Models

To understand the optimization challenges, we must first analyze the technical architecture of these flagship models. They typically adopt a native multimodal design, supporting ultra-long contexts of up to a million tokens and image understanding. Architecturally, their core characteristics are twofold:

1. Sparse Expert Architecture

To avoid linear growth in computation despite a massive 2.8 trillion parameter scale, the model employs a sparse MoE architecture. Assuming the model has 896 routing experts, dynamic routing activates only 16 experts per token during forward propagation. This means that although the model capacity is enormous, the computation required for a single forward pass is equivalent to a dense model with only tens of billions of parameters.

2. Hybrid Attention Mechanism

To maintain efficient computation and memory usage within a million-token context, the model abandons traditional pure standard attention in favor of a hybrid architecture combining KDA (Linear Attention) and Gated DSA (Sparse Attention). Specifically, about three-quarters of the attention modules use KDA linear attention to achieve O(N) computational complexity. The remaining modules utilize a Top-2048 sparse attention mechanism (DSA), significantly reducing computational overhead while preserving the ability to capture long-range dependencies.

2. Core Challenges in Adapting Ultra-Large MoE Models

In engineering practice, deploying a model with this architecture onto an accelerator card cluster introduces severe challenges across three dimensions: computation, communication, and memory.

1. Computation: Scheduling Difficulties from Complex Memory Access Patterns

KDA linear attention relies on state recursion and chunked computation, featuring continuous data access patterns. Conversely, DSA sparse attention requires fetching non-contiguous token blocks via sparse indices. Interleaving these two drastically different memory access patterns within a single model significantly increases the difficulty of operator fusion. Traditional small operators lead to frequent kernel launch overheads and memory read/write bottlenecks, severely dragging down execution efficiency.

2. Communication: Expert Load Imbalance and Communication Amplification

In a sparse routing scenario with 896 experts, the hit frequency for different experts is highly uneven. This leads to uneven token distribution among experts and small computation batches for individual experts. On one hand, load imbalance causes severe compute idling (some cards waiting for others). On the other hand, the MoE structure inherently requires All-to-All communication to dispatch tokens, and an ultra-large number of experts causes communication overhead to rise exponentially.

3. Memory: The Memory Wall of Ultra-Large Weights

A 2.8T parameter model requires approximately 5.6TB of memory space just to store weights in BF16 precision. Even using commercial AI accelerator cards with the largest available memory, a single card cannot hold the model, necessitating distributed storage across multiple cards. How to partition these parameters across cards and efficiently aggregate them during computation is the primary challenge in system adaptation.

3. Full-Stack System-Level Optimization Solutions

To address these challenges, leveraging hardware architectural advantages like high bandwidth, low latency, and unified memory addressing, the industry (including underlying compute platforms like Baidu AI Cloud) has built a full-stack optimization solution. This covers “model adaptation, mixed parallelism, operator acceleration, communication optimization, and memory management,” utilizing an Agent-driven automated workflow to achieve day-level integration for new models.

1. Memory and Parallelism Strategy: FSDP2 + EP Hybrid Architecture

Faced with the 5.6TB memory wall, traditional Tensor Parallelism (TP) generates massive communication overhead at the trillion-parameter scale. The current best practice is a hybrid architecture utilizing FSDP2 (Fully Sharded Data Parallel 2.0) and EP (Expert Parallelism).

• FSDP2 Mechanism: Unlike FSDP1, which shards by layer, FSDP2 implements finer-grained parameter sharding, splitting each parameter tensor across different cards. It asynchronously All-Gathers parameters before forward computation and immediately Reduce-Scatters gradients after backward computation, releasing the parameters afterward. This significantly lowers peak memory usage.
• EP64 Deployment: For the 896 routing experts, an EP64 strategy is used, distributing experts across 64 cards (approximately 14 experts per card). Non-expert layers (like Attention) are managed by FSDP2, while expert layers are managed by EP, achieving a reasonable load partition.

Below is a pseudo-code example of advanced hybrid parallelism based on PyTorch Distributed:

import torch.distributed as dist
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
# Assume the process group is initialized and topology is configured
# EP64 means the expert parallel dimension is 64 cards
mesh = dist.init_device_mesh("cuda", (world_size // 64, 64))
model = MegaMoEModel(num_experts=896, top_k=16)
# Apply FSDP2 to non-expert layers for parameter sharding
for layer in model.attention_layers:
    layer = FSDP(layer, mesh=mesh["data"], sharding_strategy="full_shard")
# Apply Expert Parallelism (EP) to MoE expert layers
# Expert parameters are partitioned along the EP dimension, each card holds only a subset of experts
model.moe_layers = apply_expert_parallel(
    model.moe_layers, 
    ep_mesh=mesh["expert"],
    num_local_experts=896 // 64
)

2. Operator-Level Fusion: Breaking the Hybrid Attention Computation Bottleneck

To address the interleaved memory access of KDA and DSA, native small operator concatenation is discarded in favor of deep operator fusion based on underlying C/C++ operator development kits (such as AscendC or similar platform tools).

• Large Forward/Backward Fused Operators: Discrete steps in KDA, such as state recursion, matrix multiplication, and activation functions, are integrated into a single kernel execution, eliminating the read/write of intermediate results to disk/memory.
• DSA Sparse Attention Fusion: For the Top-2048 sparse mechanism, a composite fused operator containing Load Index, Sparse Fetch, Attention Compute, and Gather (LI/SFA/SFAG) is developed. By reusing shared memory (L1/L2 Cache) and vectorized loading for non-contiguous memory access, multiple small-scale IOs are compressed into a single large-block pipeline computation. Compared to stacking original small operators, this solution achieves a performance improvement of over 2x.

3. System-Level Communication and Scheduling Optimization

• All-to-All Communication Optimization: Leveraging the high bandwidth advantages of underlying hardware RoCE or HCCS super-node interconnects, the underlying All-to-All communication primitives are rewritten. RDMA technology is used to bypass CPU intervention, achieving low-latency direct token routing among experts across multiple cards.
• Dynamic Load Balancing Scheduling: Noise and load losses are introduced at the MoE routing layer, combined with system-level dynamic micro-batch scheduling. This aligns tokens routed to the same expert on the timeline as much as possible, increasing the actual computation Batch Size for partial experts and improving MAC array utilization.

4. Typical Application Scenarios and Empirical Validation

Relying on large-scale joint reinforcement learning and system-level optimization, these trillion-scale multimodal models have demonstrated outstanding capabilities in multiple high-value domains. Optimized with the FSDP2 + EP64 architecture, the model has successfully completed LoRA fine-tuning training validation on a 128-card super cluster, proving highly valuable for engineering deployment.

1. Long-Range Autonomous Programming and Agent Tasks: The million-token context and robust sparse routing enable the model to maintain context memory of ultra-long code repositories, supporting complex multi-step Agent tool calls and autonomous repairs.
2. Financial Research and Deep Knowledge Work: The linear attention mechanism reduces the parsing cost of long texts, allowing the model to ingest hundreds of financial reports or research papers at once and rapidly generate deep analysis reports.
3. 3D Creation and Native Multimodal: The native fusion of image understanding and generation, combined with ultra-large parameter capacity, exhibits finer semantic understanding capabilities in 3D scene reconstruction and cross-modal creation.

5. Conclusion and Future Outlook

Adapting ultra-large sparse MoE multimodal models is not only a test of algorithmic architecture but also an extreme stress test of underlying system software engineering capabilities. Through the FSDP2 + EP hybrid parallel deployment, large-granularity operator fusion for hybrid attention, and Agent-driven automated development workflows, we have successfully broken through the 5.6TB memory wall and the computation bottleneck of non-contiguous memory access.

In the future, as model parameters evolve toward the ten-trillion level, underlying compute platforms will further advance toward super-node architectures, unified memory addressing, and higher-bandwidth inter-chip interconnects. In this process, deeply engaged system-level optimization and software-hardware co-design will become the core cornerstone supporting the industrial-scale deployment of large models.