Training and Fine-Tuning Native Multimodal Trillion-Parameter MoE Models: A Practical Guide

As large models push toward trillion-parameter scales and evolve toward native multimodality and ultra-long contexts, the underlying training and fine-tuning infrastructure faces unprecedented challenges. The emergence of a new generation of 3-trillion-scale open-weight native multimodal models (with total parameter counts reaching 2.8 trillion) signals a deepening coupling of Stable Latent MoE sparse expert architectures with hybrid attention mechanisms. This article sets aside specific brands and product attributes to analyze, from a general technical perspective, the core challenges these ultra-large-scale MoE models present during training and fine-tuning, and to offer system-level, full-stack optimization practices.

1. Architectural Characteristics and Training Challenges of Ultra-Large-Scale MoE Models

These flagship models typically adopt a sparse expert architecture with nearly 900 routed experts, activating only about 16 experts per token. For attention, they use a hybrid of KDA (linear attention) and Gated DSA (sparse attention), where roughly three-quarters of the attention blocks use KDA, combined with Top-2048 sparse attention, to support contexts of up to 1 million tokens and native image understanding.

This extreme parameter and architectural design translates directly into three fundamental technical challenges:

1. Compute side: complex operator fusion and scheduling difficulties
   KDA’s state recurrence and chunked computation intertwine with DSA’s sparse indexing and non-contiguous memory access, greatly increasing the difficulty of operator fusion. Traditional fine-grained small-operator serial execution leads to frequent kernel launch overhead and memory access bottlenecks.
2. Communication side: sparse routing causes load imbalance
   Sparse routing across hundreds of experts is highly prone to uneven token distribution. When certain experts are activated at high frequency, the per-expert compute batch becomes too small, preventing core computations like Grouped GEMM from fully utilizing available compute, while All-to-All communication is susceptible to long-tail latency.
3. Memory side: weight and optimizer state overflow
   At 2.8 trillion parameters, BF16 weights alone occupy approximately 5.6 TB of memory. Adding optimizer states, gradients, and activations, the memory requirement easily reaches tens of terabytes—far beyond the physical limits of a single machine or even an ordinary cluster.

2. Full-Stack Parallelism Strategy and Memory Management Practices

Facing these challenges, the underlying compute platform must leverage architectural advantages such as high bandwidth, low latency, and unified memory addressing to build full-stack training capabilities covering model adaptation, hybrid parallelism, and memory management.

2.1 FSDP2 + EP Hybrid Parallel Architecture

To resolve the tension between the memory wall and compute efficiency, the industry best practice is a hybrid parallel architecture combining FSDP2 (Fully Sharded Data Parallel 2) with EP (Expert Parallel).

• Distributed parameter deployment and aggregation: FSDP2 reduces per-GPU memory pressure through finer-grained sharding of parameters, gradients, and optimizer states. For MoE structures, EP distributes different experts across different compute nodes.
• Parallel configuration in practice: On a 128-GPU cluster, an FSDP2 + EP64 configuration is typically used for LoRA fine-tuning validation. This configuration maximizes communication bandwidth utilization while ensuring each GPU receives a sufficient compute batch.

2.2 Dual Optimization for Large-Vocabulary Memory

Ultra-large models typically come with large vocabularies, causing memory and compute spikes in the Embedding and Logits computation layers. To address this bottleneck, two complementary optimizations can be applied: Chunk Loss and CCE Loss.

• Chunk Loss: Chunks the loss computation along the sequence dimension, avoiding the need to keep activations for an entire ultra-long sequence resident in memory at once, significantly reducing peak memory usage.
• CCE Loss: Chunks computation along the vocabulary dimension, decomposing the otherwise enormous Logits matrix multiplication into multiple smaller matrices, resolving OOM issues caused by large vocabularies.

3. Operator-Level Deep Fusion and Performance Tuning

On the core compute and communication paths, coarse-grained parallelism alone is insufficient to fully exploit hardware performance—optimization must go deeper to the operator level for fusion and acceleration.

3.1 Deep Fusion of Attention Mechanisms

• For KDA linear attention: Design large forward and large backward fused operators. Integrate multiple stages such as state updates and chunked computation into a single kernel, reducing intermediate result writes and memory access overhead.
• For DSA sparse attention: Use fused AscendC operators such as LI, SFA, and SFAG to replace the original combinations of small operators. Empirical results show that deep fusion can deliver more than 2x performance improvement.
• AttnRes deep residual mechanism: For such innovative structures, general-purpose compilation frameworks like Triton and compilers like BiSheng can be used to rapidly complete forward and backward operator development and integration, shortening the adaptation cycle for new architectures.

The following pseudocode illustrates the general logic of implementing a custom fused operator based on the Triton framework, for developer reference:

import triton
import triton.language as tl
@triton.jit
def fused_kda_attention_kernel(
    Q_ptr, K_ptr, V_ptr, State_ptr, Out_ptr,
    seq_len, hidden_dim,
    BLOCK_SEQ: tl.constexpr, BLOCK_HIDDEN: tl.constexpr
):
    # Compute indices for sequence and hidden dimensions
    pid_seq = tl.program_id(0)
    pid_hidden = tl.program_id(1)
    # Fuse state recurrence with chunked computation
    offs_seq = pid_seq * BLOCK_SEQ + tl.arange(0, BLOCK_SEQ)
    offs_hidden = pid_hidden * BLOCK_HIDDEN + tl.arange(0, BLOCK_HIDDEN)
    # Load Q, K, V data (optimized for non-contiguous memory access)
    q = tl.load(Q_ptr + offs_seq * hidden_dim + offs_hidden)
    # ... linear attention state update logic ...
    # Write results back to Out, avoiding separate memory allocation for intermediate results
    tl.store(Out_ptr + offs_seq * hidden_dim + offs_hidden, result)

3.2 Full-Pipeline MoE Operator Optimization

MoE model forward and backward propagation relies on three core stages: Token Dispatch, Expert Compute, and Combine. To address full-pipeline performance bottlenecks, the following optimizations are needed:

• Core operator fusion: Deeply fuse data rearrangement operators such as Permute and Unpermute with Grouped GEMM to reduce kernel switching overhead.
• Communication-computation overlap: Leverage hardware dual-stream or multi-stream mechanisms to overlap All-to-All communication with expert computation in the time dimension, hiding communication latency.
• Expert load balancing: Introduce fine-grained load monitoring and dynamic routing adjustments during the Token Dispatch stage to prevent some experts from being overloaded while others sit idle.

4. System-Level Optimization Mechanisms and Automated Development

Beyond individual operator optimizations, system-level scheduling mechanisms and engineering of development workflows also determine training efficiency and integration speed for large models.

4.1 Operator-Level Selective Caching Mechanism

During large model training, the computation results of certain MatMul or GMM operators are reused multiple times in forward or backward passes. Introducing a Swap Core mechanism allows selective L2-level (secondary cache) storage of computation results for designated operators.

This mechanism supports tiered storage scheduling between HBM (high-bandwidth memory) and DDR (conventional memory/host memory). For operator outputs with high reuse rates that are temporarily not participating in computation, they can be proactively swapped to DDR and asynchronously loaded back to HBM when needed. Under commonly tested configurations, this mechanism can improve training performance by an average of 15%.

4.2 Automated Development and Rapid Integration Workflow

Given the rapid iteration of large model architectures (e.g., from text-only to multimodal, from standard attention to hybrid attention), the traditional manual operator adaptation cycle often takes weeks. By introducing an agent-driven automated development workflow, day-level integration of new models becomes achievable.

This workflow typically includes:

1. Graph structure parsing: Automated tools read the model computation graph and identify unsupported custom operators.
2. Code generation and compilation: Based on compilation frameworks such as Triton Ascend, an initial version of operator code is automatically generated and compiled.
3. Precision alignment and tuning: Test cases are automatically injected, precision alignment is performed against reference outputs, and parameters such as tile sizes are automatically adjusted based on performance models.

5. Summary

Training the new generation of 3-trillion-scale native multimodal MoE models is not merely a contest of algorithmic capability—it is a stress test of underlying systems engineering and compute scheduling limits. By building an FSDP2 + EP hybrid parallel architecture, deep operator fusion, Swap Core tiered caching, and dual optimization mechanisms for large vocabularies, the three major bottlenecks of compute, communication, and memory can be effectively broken. With automated development workflows, the underlying infrastructure can respond to new model integration requirements at day-level speed, providing a solid compute foundation for advanced applications such as long-horizon autonomous programming, complex agent tasks, and 3D creation.