Technical Path and Practices for Adapting Trillion-Parameter LLMs on Domestic Computing Platforms

1. Background: Adaptation Challenges from the Scaling of Large Models

In recent years, the parameter scale of large language models has continued to climb, from tens of billions to hundreds of billions, and now to the trillion-level (e.g., 3 trillion parameters). Model capabilities have achieved qualitative leaps in dimensions such as code generation, complex reasoning, and long-range task planning. Taking a recent class of ultra-large open-source models as an example, they support a context window of up to 1 million tokens. After massive post-training and reinforcement learning, they have achieved results approaching top-tier closed-source models in benchmarks like terminal operation and code generation.

However, this leap in model scale also brings severe challenges to the underlying computing platforms:

• Memory Wall: Even with FP8/INT8 quantization, a 3-trillion-parameter model still requires tens or even hundreds of GB of storage for weights alone, exceeding the capacity of a single GPU.
• Computational Density: Ultra-long contexts mean that the KV Cache for attention computation grows linearly or even super-linearly, placing extremely high demands on memory bandwidth and compute unit utilization.
• Communication Bottlenecks: During multi-GPU parallel inference, the cross-device communication overhead introduced by tensor parallelism and pipeline parallelism cannot be ignored.
• Software Stack Maturity: Domestic computing platforms require full-stack adaptation—from operator libraries and compilers to inference frameworks—to ensure that cutting-edge models “run, run stably, and run efficiently.”

This article focuses on the adaptation practices of trillion-parameter models on domestic AI training and inference unified computing cards, breaking down core technical segments such as inference path migration, memory optimization, operator adaptation, and stability assurance.

2. Model Architecture Characteristics and Computational Analysis

2.1 Sparsification and MoE Architecture

Trillion-parameter models generally adopt the Mixture of Experts (MoE) architecture. Through a sparse activation mechanism, they keep the actual computation per inference within a reasonable range while maintaining a massive total parameter count. In a typical MoE model, although the total parameters reach the 3-trillion level, the expert parameters actually activated per token might only account for a small fraction.

This architecture poses special requirements for the inference system:

• Expert Routing and Load Balancing: Requires efficient implementation of dynamic token-to-expert routing while striving to ensure balanced computational loads across experts.
• Expert Weight Residency Strategy: Due to the large number of experts, it is impossible to keep all expert weights resident in a single GPU’s memory simultaneously. A sensible weight swapping mechanism must be designed.
• All-to-All Communication: Under expert parallelism, tokens must be distributed and aggregated across different devices, making the communication pattern more complex than traditional dense models.

2.2 Long Context Attention Mechanism

Supporting a 1-million-token context means that the computational and storage demands of the attention layer increase dramatically. The industry typically employs the following combination of techniques to address this:

• Grouped-Query Attention (GQA) or Multi-Query Attention (MQA): Reduces the number of KV heads, lowering the memory footprint of the KV Cache.
• Sliding Window Attention Mixed with Global Attention: Controls computation while maintaining long-range dependency modeling capabilities.
• KV Cache Quantization and Paged Management: Stores the KV Cache in low precision and uses a paging mechanism to achieve dynamic memory allocation.

During adaptation, it is necessary to implement efficient operators for each of these attention variants individually and ensure that numerical precision meets the model’s inference requirements.

3. Key Technical Paths for Domestic Computing Platform Adaptation

3.1 Overall Software Stack Architecture

Domestic AI accelerator cards are typically equipped with a self-developed software stack, covering the driver layer, runtime, operator library, communication library, and inference framework adaptation layer. Taking a domestic training and inference unified card as an example, its software stack needs to complete the integration of the following key modules when adapting trillion-level models:

┌─────────────────────────────────────────┐
│      Inference Framework (e.g., vLLM/SGLang)      │
├─────────────────────────────────────────┤
│      Model Adaptation Layer (Model Definition/Weight Loading)        │
├─────────────────────────────────────────┤
│   Operator Library (Attention/GEMM/MoE Router etc.)   │
├─────────────────────────────────────────┤
│   Communication Library (All-Reduce/All-to-All)         │
├─────────────────────────────────────────┤
│   Runtime and Driver (Memory Management/Stream Scheduling)           │
└─────────────────────────────────────────┘

The core of the adaptation work lies in mapping every type of computational operation in the model to an efficient operator implementation on the target hardware, ensuring the correctness and stability of the end-to-end inference process.

3.2 Inference Path Migration and Operator Adaptation

The inference path of a trillion-level model typically contains the following key operators:

1. Embedding Layer: Token embedding lookup and positional encoding generation.
2. Attention Layer: QKV projection, RoPE positional encoding, FlashAttention-style block attention computation, and output projection.
3. MoE Layer: Gating network, token routing, expert FFN computation, and result aggregation.
4. Normalization Layer: RMSNorm, etc.
5. Output Layer: LM Head projection and sampling.

On the domestic computing platform, efficient versions of these operators must be implemented one by one. Taking the attention operator as an example, it is necessary to design appropriate tiling strategies and data transfer pipelines based on the matrix compute unit characteristics of the target hardware to hide memory access latency.

# Pseudo-code: Block computation logic for the attention operator
def paged_attention(query, key_cache, value_cache, block_table, seq_len):
    # Read KV Cache by page to avoid loading all KV at once
    for block_idx in block_table:
        k_block = load_kv_block(key_cache, block_idx)
        v_block = load_kv_block(value_cache, block_idx)
        # Compute attention scores and weighted sum in blocks
        score = matmul(query, k_block.transpose())
        score = softmax_with_mask(score, seq_len)
        output += matmul(score, v_block)
    return output

The paged attention mechanism above can effectively support ultra-long context scenarios, preventing memory overflow (OOM) caused by an excessively large KV Cache.

3.3 Memory Optimization and Multi-Card Parallel Strategies

The memory requirement of a 3-trillion-parameter model far exceeds single-card capacity, making multi-card parallel inference mandatory. Common parallel strategy combinations include:

• Tensor Parallelism (TP): Splits the weights of a single matrix multiplication across multiple cards, reducing the weight storage pressure on a single card.
• Pipeline Parallelism (PP): Splits the model by layers, with different cards responsible for different layer-level computations.
• Expert Parallelism (EP): Distributes MoE experts across different cards, utilizing All-to-All communication for token distribution and aggregation.
• Data Parallelism (DP): Increases throughput in multi-replica scenarios.

In actual deployment, the optimal parallel combination must be selected based on the model structure, hardware topology, and inference business characteristics. For example, for models with a high proportion of MoE layers, the EP degree needs to match the number of experts; for attention layers, the TP degree is limited by the number of KV heads.

In addition, memory management must also consider:

• Offline Weight Quantization: Quantizing weights from FP16 to INT8/FP8, halving memory usage.
• Dynamic KV Cache Allocation: Allocating memory based on the actual sequence length of the request to avoid reservation waste.
• Activation Recomputation: Trading recomputation of intermediate activations for memory space when memory is constrained.

3.4 Communication Optimization

In multi-card parallel inference, communication overhead often becomes the performance bottleneck. For domestic computing platforms, the following communication paths need optimization:

• All-Reduce: Used for gradient or activation synchronization in TP, can be optimized via Ring All-Reduce or Tree All-Reduce algorithms.
• All-to-All: Used for token distribution and aggregation in EP, requiring efficient communication scheduling designed based on network topology.
• P2P Communication: Used for inter-layer activation transfer in PP, where latency can be hidden through compute-communication overlap.

In practical engineering, achieving ideal scaling efficiency usually requires combining the hardware’s high-speed interconnect capabilities (e.g., high-speed inter-card links) with software stack communication library optimizations.

4. Stability Assurance and Performance Tuning

4.1 Numerical Precision and Consistency

Trillion-parameter model inference is sensitive to numerical precision, especially in long-context scenarios where accumulated errors in attention scores can impact generation quality. The adaptation process requires:

• Conducting precision alignment tests on key operators to ensure output errors are within acceptable limits compared to reference implementations (e.g., standard CPU or GPU implementations).
• Adopting mixed-precision strategies during quantized inference, retaining high-precision computation for sensitive layers.
• Implementing numerical overflow detection and protection mechanisms to prevent inference interruptions caused by Inf/NaN.

4.2 Long-Term Stability Testing and Fault Recovery

Large-scale inference services need to run stably for long periods, necessitating rigorous long-term stability testing:

• Continuous Stress Testing: Running under full load for dozens of hours, monitoring metrics like memory leaks, temperature, and power consumption.
• Fault Injection Testing: Simulating anomaly scenarios like single-card failures and network jitter to verify system fault tolerance and recovery capabilities.
• Request-Level Isolation: Ensuring that an exception in a single request does not affect the processing of other requests.

4.3 Performance Tuning Practices

Premised on ensuring stability, performance tuning typically expands from the following dimensions:

• Batching Strategy: Continuous batching can significantly increase throughput, but batch sizes must be controlled reasonably to avoid memory overflow.
• Operator Fusion: Fusing multiple small operators into a larger one to reduce kernel launch overhead and memory access frequency.
• Compute-Communication Overlap: Executing other computational tasks while waiting for communication to complete, improving hardware utilization.
• KV Cache Management Optimization: Utilizing techniques like prefix sharing and cache reuse to reduce redundant computation.

5. Summary and Outlook

Adapting trillion-parameter LLMs is a systems engineering effort involving multiple levels: model architecture analysis, operator implementation, parallel strategy design, memory management, communication optimization, and stability assurance. By continuously improving software stack capabilities, domestic computing platforms are now capable of supporting such cutting-edge models to run stably on unified training and inference cards, unleashing the model’s expected capabilities in scenarios like code generation and long-range Agent tasks.

Moving forward, as model scales continue to grow and context windows expand further, domestic computing platforms must continue investing in the following directions:

• More Efficient Sparse Computation Support: Further optimizing expert scheduling and communication efficiency for sparse architectures like MoE.
• Smarter Memory Management: Combining request features to predict memory demands, enabling more refined dynamic allocation.
• More Complete Ecological Toolchains: Providing full-process tool support from model conversion and quantization compression to deployment and O&M, lowering adaptation costs for developers.

For enterprise users and developers, choosing a domestic computing platform equipped with a mature software stack and continuous adaptation capabilities will help rapidly build efficient and stable AI inference services in the era of large models.