Full-Stack Adaptation of Trillion-Parameter MoE Models: Engineering Practices for Training and Fine-Tuning

As open-weight models push toward the trillion-parameter mark, their architectures have shifted from dense designs to complex forms that combine sparse Mixture-of-Experts (MoE) routing with hybrid attention. These models dramatically extend the capability frontier, but they also pose unprecedented engineering challenges for training and inference infrastructure: ballooning parameter counts make the memory wall more acute, sparse routing creates load imbalance, and interleaved attention mechanisms make operator fusion and scheduling genuinely difficult. This article uses a natively multimodal open-weight model with 2.8 trillion total parameters as a working example, walking through its adaptation and fine-tuning on a supernode computing platform and distilling a reusable engineering methodology.

1. Model Architecture and Engineering Challenges

The model adopts a Stable Latent MoE sparse expert architecture with 896 routed experts, of which only 16 are activated per token. On the attention side, it pairs KDA linear attention with gated sparse attention (Gated DSA): roughly three quarters of the attention blocks use KDA linear attention, combined with a Top-2048 sparse attention mechanism, supporting a context window of up to 1 million tokens and native image understanding. Capability-wise, built on large-scale joint reinforcement learning and system-level optimization, the model performs strongly in long-horizon autonomous coding, complex agent tasks, 3D creation, financial research, and deep knowledge work.

From an engineering standpoint, this architecture decomposes into three core challenges:

Compute: sharply increased operator fusion and scheduling complexity. KDA linear attention consists of two stages—state recurrence and chunked computation—with strict data dependencies between them. DSA sparse attention involves sparse index construction and non-contiguous memory access, resulting in highly irregular access patterns. Interleaving these two mechanisms in a single model means that large numbers of small operators must be consolidated into unified kernels; otherwise, scheduling overhead and memory traffic will swallow the compute gains.

Communication: load balancing under sparse routing. With 896 experts, sparse routing easily produces uneven token distributions: some experts receive far more tokens than the average, while others run small batches at low utilization. This both wastes compute and amplifies the tail latency of All-to-All communication.

Memory: weight scale approaching hardware limits. Storing 2.8 trillion parameters in BF16 alone takes roughly 5.6 TB—clearly far beyond any single card—so expert parameters must be deployed in a distributed fashion and aggregated efficiently.

These three challenges are tightly coupled: memory constraints dictate the choice of parallelism strategy, the parallelism strategy shapes communication patterns, and communication efficiency in turn limits the granularity of operator fusion. Adaptation work therefore cannot optimize isolated points; it requires full-stack capability spanning model adaptation, hybrid parallelism, operator acceleration, communication optimization, and memory management.

2. Hybrid Parallelism: Distributing Expert Parameters

To address the weight-scale problem, the practice uses a hybrid parallel architecture combining FSDP2 with expert parallelism (EP). FSDP2 shards non-expert parameters (such as attention and embedding layers) and synchronizes their gradients, while EP slices the 896 experts across devices along the expert dimension, enabling distributed placement and efficient aggregation of expert parameters.

Two design points are critical. First, the expert parallelism degree must evenly divide the total expert count, avoiding “expert holes” after sharding. Second, FSDP2’s shard groups and EP’s communication groups must be addressed in a unified scheme to reduce inter-group communication hops. In validation, LoRA fine-tuning was completed on a 128-accelerator supernode using an FSDP2 + EP64 configuration—meaning an expert parallelism degree of 64, with each expert sharded across 64 device groups for collaborative computation and non-expert parameters managed by FSDP2 sharding.

LoRA fine-tuning holds particular value in this setting: it freezes backbone weights and trains only low-rank adapter matrices, compressing trainable parameters by several orders of magnitude. Memory pressure therefore concentrates on forward activations and the intermediate tensors of expert routing rather than on gradients and optimizer states—making fine-tuning of a trillion-scale model feasible on a limited number of cards.

3. Operator Acceleration: From Fragmented Small Operators to Fused Kernels

Operator-level optimization proceeds along two tracks, one per attention mechanism.

KDA linear attention: fused large-forward and large-backward kernels. KDA’s state recurrence is temporally dependent, and its chunked computation requires intra-block local reduction. A conventional implementation splits this into many small operators, forcing every stage to round-trip intermediate state through memory and turning bandwidth into the bottleneck. In practice, large fused forward and backward operators were designed to fold state recurrence, chunked reduction, output projection, and the other computation stages into a single kernel, keeping intermediate results in on-chip memory instead of writing them back to main memory and significantly reducing memory access count.

DSA sparse attention: a dedicated fused-operator family. DSA’s sparse index construction, non-contiguous access, and gated computation are difficult to express efficiently with generic operators. A set of AscendC-based fused operators—LI, SFA, SFAG, and related variants—was used to integrate the model, merging index generation, sparse gather, and gated weighting into single kernels and delivering more than 2x performance over the original composition of small operators. The gain comes from two sources: fewer kernel launches and less scheduling overhead, and locality reordering of non-contiguous access patterns inside the operator.

The fused-operator structure can be abstracted as follows:

# Pseudocode: KDA fused forward
def kda_fused_forward(q, k, v, state, block_size):
    # Stage 1: intra-block local reduction
    local = block_reduce(q, k, v, block_size)
    # Stage 2: cross-block state recurrence (state kept on-chip)
    state, out_local = recurrent_update(local, state)
    # Stage 3: output projection
    return project(out_local), state

The core idea of fusion is to keep inter-stage data on the chip rather than using main memory as an intermediary at every step.

4. Communication Optimization: Load Governance for Sparse Routing

For 896-expert sparse routing, communication optimization centers on mitigating uneven token distribution. In practice, measures are applied at three layers:

• Routing layer: introduce capacity factors together with token dropping/reshuffling policies, redistributing tokens that exceed an expert’s capacity to suppress hot experts.
• Communication layer: overlap All-to-All communication with compute and send tokens in batches grouped by expert, avoiding the long tail caused by a single large communication.
• Scheduling layer: for experts with small computation batches, merge tokens destined for the same expert across multiple micro-batches to improve matrix multiplication efficiency.

The supernode architecture’s high bandwidth, low latency, and unified memory addressing play an important role here: unified addressing lets cross-device access to expert parameters proceed without explicit copies, reducing aggregation overhead, while low latency makes fine-grained communication splitting a viable option—trading many smaller communication batches for better load balance.

5. Memory Management: A Tiered Strategy and Automated Onboarding

Memory management follows a tiered strategy: backbone weights are stored in a distributed BF16 layout, LoRA adapter parameters stay resident, and activation peaks are controlled through recomputation and chunked scheduling. Combined with FSDP2 sharding, per-card memory decomposes into three parts—sharded weights, sharded gradients, and local activations—and each part can be tuned independently if it exceeds its budget.

On the process side, an agent-driven automated development workflow achieves day-level onboarding and rapid iteration for new models: from weight parsing, structure mapping, and parallel-strategy generation to fused-operator matching, most steps are completed by automated tooling, while engineers focus on designing novel operators and performance tuning. This workflow significantly shortens the window between a model’s open-source release and its being trainable on the platform.

6. Key Takeaways

Drawing the practice together, adapting trillion-scale sparse MoE models comes down to four lessons:

1. Parallelism strategy first: settle the split between EP and FSDP2 before touching operator optimization; otherwise the optimization target drifts whenever the parallel plan changes.
2. Design fused operators by attention type: for linear attention, the focus of fusion is state residency; for sparse attention, it is memory-access locality.
3. Couple communication optimization with routing policy: optimizing the communication library alone, without governing hot experts, yields limited gains.
4. Automation is the prerequisite for onboarding at scale: model architectures iterate quickly, so day-level onboarding capability carries more long-term value than extreme single-point optimization.

Infrastructure adaptation for ultra-large models is a systems engineering effort in which compute, communication, and memory constrain one another. Only by orchestrating parallelism, operators, and scheduling from a full-stack perspective can training for trillion-parameter models actually run on the compute that is available.