Architecture and Engineering Practice for Trillion-Parameter MoE Multimodal Models

Introduction: Three Challenges for Ultra-Large Sparse Models

As model parameters approach the trillion scale, the industry has reached a consensus: simply scaling dense architectures is no longer sustainable. Sparse Mixture-of-Experts (MoE) architectures—which activate only a subset of parameters during inference—have become the mainstream choice for ultra-large models by maintaining model capacity while controlling computational cost. However, when total parameters reach the trillion level and activated parameters exceed 100 billion, engineering teams face three simultaneous challenges:

1. Computational efficiency with extremely long contexts: When sequence length reaches millions of tokens, the quadratic complexity of standard attention becomes an insurmountable bottleneck.
2. Training stability with massive expert counts: When the number of routed experts grows to hundreds or even nearly a thousand, issues such as load imbalance, routing collapse, and gradient oscillation become significantly amplified.
3. Long-horizon agentic reasoning: Tasks involving multi-step tool calls and long-chain planning require the model to maintain state consistency and reasoning coherence across extremely long contexts.

This article systematically reviews a technical approach for trillion-parameter MoE multimodal models, covering attention module fusion design, sparse expert routing architecture, and multi-stage pretraining and post-training strategies.

Overall Architecture Overview

The model adopts an MoE multimodal architecture with 2.8 trillion total parameters, approximately 104 billion activated parameters per forward pass, native vision multimodal input support, and a 1-million-token context window. Its core design can be summarized across four layers:

• Attention layer: Integrates incremental attention (KDA) with gated multi-head latent attention (Gated MLA), and uses attention residual connections (AttnRes) for cross-layer information sharing.
• MoE layer: Employs a stable latent MoE architecture with 896 routed experts, combined with the SiTU-GLU activation function and a quantile balancing (QB) mechanism.
• Pretraining: Adopts a multi-stage context expansion strategy from 8K to 64K to 256K to 1M tokens.
• Post-training: Applies million-token-level agentic reinforcement learning to strengthen long-horizon reasoning and tool-use capabilities.

Each layer is examined in detail below.

Attention Module: Balancing Efficiency and Expressiveness

Incremental Attention (KDA)

The computational cost of standard self-attention grows quadratically with sequence length. At the million-token scale, both the memory footprint of the KV cache and the attention matrix computation become bottlenecks. Incremental attention (KDA) maintains a compressed representation of key-value states and performs incremental updates only for newly added tokens, reducing long-sequence attention computation from quadratic to near-linear complexity.

In engineering practice, KDA is typically implemented with a chunked computation strategy:

# Pseudocode: chunked incremental attention
def chunked_delta_attention(Q, K, V, chunk_size, state=None):
    outputs = []
    for i in range(0, seq_len, chunk_size):
        q_chunk = Q[i:i+chunk_size]
        k_chunk = K[i:i+chunk_size]
        v_chunk = V[i:i+chunk_size]
        # Standard attention within chunk + incremental state update across chunks
        o_chunk, state = delta_update(q_chunk, k_chunk, v_chunk, state)
        outputs.append(o_chunk)
    return concat(outputs), state

This design preserves long-range dependency modeling while significantly reducing memory and computational overhead.

Gated Multi-Head Latent Attention (Gated MLA)

Multi-head latent attention (MLA) compresses the KV cache by projecting keys and values into a low-dimensional latent space. The addition of a gating mechanism allows the model to dynamically adjust the contribution weights of different attention heads, preventing some heads from being dominated by noise in long contexts.

Key elements of gated MLA:

• Latent compression: KV projections are compressed into low-dimensional latent variables, reducing cache size.
• Gated modulation: Input-dependent gating coefficients weight the attention outputs, improving focus on critical information.

Attention Residuals (AttnRes) and Cross-Layer Sharing

In deep networks, gradient vanishing and information decay weaken information transfer between distant layers. Attention residuals (AttnRes) pass shallow-layer attention representations directly to deeper layers through residual paths, enabling cross-layer information sharing. This mechanism is especially important for ultra-long contexts—local details captured at lower layers can influence global decisions at higher layers through the residual path.

MoE Layer: Stable Training under Extreme Sparsity

Stable Latent MoE Architecture

The MoE side of this model carries 896 routed experts. At such an extreme level of sparsity, traditional Top-K routing is prone to load imbalance and expert utilization collapse. The core ideas of Stable Latent MoE include:

• Latent-space routing: Routing scores are computed in a low-dimensional latent space, reducing routing overhead and improving generalization.
• Stability constraints: Regularization is applied to the routing distribution to prevent a few experts from being over-activated.

SiTU-GLU Activation Function

The choice of activation function directly affects the expressiveness of expert networks and training stability. SiTU-GLU introduces a smooth gating unit on top of GLU, mitigating activation distribution shifts under extreme sparsity conditions and helping maintain stable gradient scales.

Quantile Balancing (QB) Mechanism

Load balancing is a core challenge in MoE training. The quantile balancing mechanism calibrates routing scores using quantile statistics and dynamically adjusts expert capacity allocation, making the number of tokens received by each expert more balanced. Compared with traditional auxiliary loss approaches, QB responds more smoothly to load fluctuations in extreme sparsity scenarios without introducing additional gradient interference.

# Pseudocode: quantile-balanced routing
def quantile_balanced_routing(scores, num_experts, top_k, quantile_stats):
    # Calibrate routing scores based on historical quantiles
    calibrated = scores - quantile_stats.threshold
    topk_idx, topk_val = topk(calibrated, k=top_k)
    # Update quantile statistics
    quantile_stats.update(calibrated)
    return topk_idx, topk_val

Multi-Stage Pretraining and Post-Training

Multi-Stage Context Expansion

Pretraining follows a progressive context expansion strategy: 8K → 64K → 256K → 1M tokens. Each stage preserves short-context capabilities while gradually introducing longer sequences, avoiding the convergence difficulties of training directly on million-length inputs. Between stages, position encoding and attention window strategies are typically adjusted to ensure smooth transitions.

Million-Token-Level Agentic RL

The post-training phase introduces agentic reinforcement learning at the million-token level. Unlike traditional RLHF, agentic RL training samples include multi-turn tool calls, environment interactions, and long-chain planning, requiring the model to maintain state consistency across extremely long contexts. The training signal design at this stage must balance:

• Task success rate: end-to-end task completion.
• Process validity: verifiability of intermediate steps.
• Efficiency constraints: avoiding redundant calls and ineffective reasoning.

Engineering Practice Recommendations

For teams looking to adopt similar approaches, the following points deserve attention:

1. Progressive scaling over one-shot jumps: Both context windows and expert counts should be expanded in stages, with stability fully validated at each stage before advancing.
2. Routing stability is a life-critical factor: Once expert counts exceed several hundred, the load-balancing mechanism matters more than model capacity itself. Introducing smooth mechanisms such as quantile calibration early is strongly recommended.
3. Attention and MoE need joint design: Long context and sparse experts are not independent problems. Efficiency gains in the attention layer free up computational budget for the MoE layer, so the two should be tuned together.
4. Post-training determines the ceiling: Pretraining establishes foundational capabilities, while long-horizon reasoning and agentic abilities depend heavily on task design and signal quality in the post-training phase.

Summary

The technical difficulty of trillion-parameter MoE multimodal models fundamentally comes down to a trade-off between efficiency, stability, and capability. By fusing incremental attention with gated latent attention in the attention layer, introducing a stable latent architecture with quantile balancing in the MoE layer, and adopting progressive strategies in both pretraining and post-training, it is possible to train, scale, and deploy ultra-large models under extreme sparsity conditions. For developers, understanding the engineering motivation behind these designs is more valuable than memorizing specific parameters.