Running Trillion-Parameter Models on Domestic Compute: A Complete Engineering Path from Structure Analysis to Inference Adaptation

1. Background: Engineering Challenges of Ultra-Large-Scale Model Inference

In recent years, the parameter scale of large language models has climbed rapidly from the tens-of-billions level to the trillions. The mixture-of-experts (MoE) architecture has become the mainstream choice for supporting this scale. Taking a recently open-sourced 3-trillion-parameter model as an example, it supports a context window of up to one million tokens and delivers notable capability gains across code generation, 3D content creation, financial analysis, knowledge QA, and long-horizon agent tasks. On code and agent benchmarks, its scores have reached or approached those of leading closed-source models—for instance, 65.7% on a terminal-interaction benchmark and 61.2% on a long-horizon code benchmark, roughly a 5-point improvement over the previous model in the same series.

However, running a model of this size in practice requires far more than algorithmic breakthroughs. Engineering adaptation on the inference side—including memory management, operator mapping, precision alignment, and distributed communication—directly determines whether the model can run stably and efficiently on a given hardware platform. This article takes a generalized technical perspective to lay out the complete engineering path for adapting trillion-parameter MoE models to domestic AI accelerator cards, offering a reference methodology for developers working on inference deployment.

2. Model Structure Analysis: The Starting Point of Adaptation

Before touching the hardware, the first task is to systematically decompose the computational graph of the target model. A 3-trillion-parameter MoE model typically contains the following core components:

2.1 Sparse Expert Layers

The core of the MoE architecture is replacing traditional FFN layers with multiple expert sub-networks, activating a small number of experts per token through a gated routing mechanism. A typical Top-K routing strategy looks like this:

# Simplified illustration of MoE routing logic
def moe_forward(hidden_states, experts, gate, top_k=2):
    # Compute routing weights
    routing_weights = softmax(gate(hidden_states), dim=-1)
    # Select Top-K experts
    topk_weights, topk_indices = topk(routing_weights, k=top_k)
    # Normalize weights
    topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
    # Dispatch tokens to the corresponding experts and aggregate outputs
    output = zeros_like(hidden_states)
    for i in range(top_k):
        expert_idx = topk_indices[:, i]
        output += topk_weights[:, i] * dispatch_and_compute(
            hidden_states, experts, expert_idx
        )
    return output

Key concerns during adaptation include: the partitioning strategy for expert parallelism, the communication overhead of token dispatch and collection, and the impact of load imbalance across experts on inference latency.

2.2 Attention Layers and Long-Context Support

Supporting million-token contexts means attention computation faces severe memory and compute bottlenecks. Common optimization techniques include:

• Grouped-query attention (GQA) : reducing the number of KV heads to lower KV cache memory usage;
• Hybrid sliding-window and global attention: using sparse attention within local windows while retaining global attention at key positions;
• KV cache quantization and paged management: managing the KV cache in pages combined with low-bit quantization to compress memory.

During adaptation, you need to confirm whether the target hardware supports Flash-Attention-style fused operators and whether it has enough on-chip cache to hold the tiled attention computation.

2.3 Normalization and Activation Functions

Different models may use RMSNorm, LayerNorm, or other normalization schemes, along with activation functions such as SwiGLU and GeGLU. Although these components are not compute-intensive, they play a key role in operator fusion and precision alignment. For example, fusing RMSNorm with the subsequent linear transformation into a single operator can effectively reduce kernel launch overhead and intermediate tensor memory reads and writes.

3. Operator Mapping and Inference Path Migration

After structure analysis, the next step is mapping the model’s computational graph onto the target hardware platform’s operator library. This process generally follows these principles:

3.1 Prioritize Vendor-Provided Fused Operators

Mainstream AI accelerator cards usually ship with highly optimized basic operator libraries (matrix multiplication, convolution, normalization, etc.) as well as fused operators (Flash Attention, Fused MLP, etc.). During adaptation, prefer calling these operators rather than implementing your own, so as to fully exploit hardware characteristics.

3.2 Developing and Registering Custom Operators

For model-specific computation patterns (such as token dispatch and aggregation in MoE routing), if the operator library has no direct counterpart, custom operators must be developed. During development, note the following:

• Memory alignment: ensure input/output tensor memory layouts meet the hardware’s alignment requirements;
• Parallel strategy: make reasonable use of the hardware’s multi-core parallelism and avoid warp divergence;
• Precision control: maintain FP32 accumulation on critical paths to prevent accumulation of precision loss.

3.3 Computational Graph Optimization

Once operator mapping is complete, the entire computational graph must be optimized, including:

• Operator fusion: merging consecutive small operators into larger ones to reduce kernel launches;
• Constant folding: computing determinable constant expressions at compile time;
• Memory reuse: analyzing tensor lifetimes and reusing memory space that is no longer needed.

4. Precision Alignment and Numerical Stability

Inference with trillion-parameter models is extremely sensitive to numerical precision. During adaptation, rigorous precision alignment verification is mandatory to ensure that outputs on domestic hardware match the reference implementation (typically running on high-end general-purpose GPUs).

4.1 Layer-by-Layer Precision Comparison

A layer-by-layer comparison is recommended, starting from the embedding layer and comparing the numerical differences of output tensors at each layer. Common metrics include:

• Max absolute error
• Relative error
• Cosine similarity

For MoE models, pay special attention to routing decision consistency—that is, whether the same token is dispatched to the same expert on domestic hardware and on the reference hardware.

4.2 Mixed-Precision Strategy

To balance precision and performance, mixed-precision inference is typically used:

# Illustration of mixed-precision inference
with autocast(dtype=torch.float16):
    # Attention computation uses FP16
    attn_output = flash_attention(q, k, v)
# Residual connection and normalization use FP32
hidden_states = layer_norm(hidden_states + attn_output)

Note that the softmax computation in MoE routing and expert weight aggregation should use FP32 to avoid routing jitter caused by insufficient precision.

4.3 Numerical Stability Under Long Contexts

With million-token contexts, the numerical range of attention scores can expand dramatically, easily causing overflow or underflow. Common countermeasures include:

• Using an online softmax algorithm to compute attention in blocks while dynamically updating the maximum;
• Pre-scaling queries and keys to control the magnitude of dot-product results;
• Retaining FP32 scaling factors in the KV cache.

5. Memory and Performance Optimization for Long-Context Inference

Million-token contexts place extremely high demands on memory capacity and bandwidth. For a 3-trillion-parameter MoE model, even with expert parallelism and quantization, KV cache memory usage can still reach tens of gigabytes. Below are key optimization directions:

5.1 Paged KV Cache Management

Borrowing the virtual-memory management concept from operating systems, the KV cache is divided into fixed-size pages that are allocated and reclaimed on demand. This effectively avoids memory fragmentation and supports dynamic context extension.

class PagedKVCache:
    def __init__(self, num_layers, num_heads, head_dim, page_size=16):
        self.page_size = page_size
        self.page_table = {}  # mapping from logical pages to physical pages
        self.free_pages = []  # list of free pages
    def allocate(self, seq_len):
        num_pages = ceil(seq_len / self.page_size)
        # Allocate from free pages or request new memory
        ...
    def free(self, seq_id):
        # Reclaim all pages occupied by this sequence
        ...

5.2 On-Demand Loading of Expert Weights

For MoE models, expert weights account for the vast majority of parameters. Keeping all expert weights resident in memory would demand an enormous amount of VRAM. One viable strategy is:

• Store expert weights in host memory or high-speed storage;
• During inference, load on demand the experts activated by routing results;
• Use a prefetch mechanism to load expert weights likely needed in the next step ahead of time.

The cost of this strategy is additional data transfer latency, so it must be carefully overlapped with the compute pipeline.

5.3 Distributed Inference and Communication Optimization

When a single card’s memory cannot hold the entire model, distributed inference strategies are required. Common parallelism schemes include:

• Tensor parallelism: splitting individual weight matrices by row or column across multiple cards;
• Pipeline parallelism: assigning different layers to different cards to form a pipeline;
• Expert parallelism: assigning different experts to different cards, with All-to-All communication completing token dispatch.

On domestic hardware platforms, the maturity and bandwidth of the communication library directly affect distributed inference efficiency. During adaptation, focus on optimizing the performance of collective communication operations such as All-to-All and All-Reduce.

6. Adaptation Validation and Performance Benchmarking

After the adaptation work above is complete, a complete validation and benchmarking system must be established:

6.1 Functional Validation

• Single-sample inference: verify the model generates output normally and that it is semantically consistent with the reference implementation;
• Batch inference: verify output stability across different batch sizes;
• Long-context inference: verify functional correctness at the maximum context length.

6.2 Performance Benchmarks

• Time to first token (TTFT) : measures responsiveness during the prefill phase;
• Time per output token (TPOT) : measures generation speed during the decode phase;
• Throughput: the number of tokens processable per unit time under a given latency constraint;
• Memory usage: peak memory at different context lengths and batch sizes.

6.3 Stability Testing

• Long-running operation: verify performance degradation after hours of continuous operation;
• Abnormal inputs: verify handling of boundary cases such as overly long inputs and empty inputs;
• Concurrency stress: verify quality of service under concurrent multi-user requests.

7. Summary and Outlook

Inference adaptation for trillion-parameter models is a systems engineering effort spanning model structure analysis, operator development, precision alignment, memory management, and distributed communication. From a generalized technical perspective, this article has laid out the complete engineering path from model analysis to performance validation. The core takeaways are:

1. Structure first: a deep understanding of MoE routing, attention mechanisms, and normalization schemes is the foundation of adaptation work;
2. Operators as the backbone: prioritize reusing vendor fused operators, develop custom operators when necessary, and optimize the computational graph;
3. Precision as the basis: ensure numerical stability and output consistency through layer-by-layer comparison and mixed-precision strategies;
4. Memory as the key constraint: paged KV cache management, on-demand expert weight loading, and distributed parallelism are the keys to breaking through memory bottlenecks;
5. Closed-loop validation: build a complete test system covering functionality, performance, and stability.

As domestic AI compute platforms continue to evolve, inference deployment of trillion-parameter and even larger models will become more efficient and accessible. For developers, mastering the engineering methodology above will help them quickly complete model adaptation across diverse hardware platforms and bring large-model capabilities into more business scenarios.