Technical Path for Adapting Domestic AI Accelerators to a 3-Trillion-Parameter Model

Challenges in the Era of Ultra-Large-Scale Models

As large language model parameter counts jump from the hundred-billion scale to the trillion and even 3-trillion scale, inference faces increasingly severe bottlenecks in memory capacity, compute throughput, and communication bandwidth. A recently open-sourced ultra-large-scale model family, for instance, reaches 3 trillion parameters, supports context windows up to 1 million tokens, and demonstrates significant capability gains across code generation, 3D content creation, financial research, knowledge-based QA, and long-horizon agent tasks.

Deploying such models places extreme demands on the underlying compute platform. The ability to achieve “Day-0 adaptation”—meaning the model can be deployed and run on the same day it is open-sourced—has become a key benchmark for measuring the maturity of an AI accelerator platform’s software stack. This article starts from the model architecture characteristics and systematically outlines the critical technical paths for adapting ultra-large-scale model inference on domestic AI accelerators.

Model Architecture and Computational Characteristics

Parameter Scale and Memory Requirements

Even when loaded in FP16/BF16 precision, the weights of a 3-trillion-parameter model require several terabytes of memory. This far exceeds single-card memory capacity, making multi-card distributed inference strategies mandatory. Common approaches include:

• Tensor Parallelism: Splitting the weight matrices of each Transformer layer across multiple cards to reduce per-card memory pressure.
• Pipeline Parallelism: Partitioning the model by layers into stages, with different stages deployed on different devices.
• Expert Parallelism: For mixture-of-experts (MoE) architectures, distributing different experts across different devices.

In practice, these strategies are typically combined to load and run the model within the memory constraints of individual accelerators.

Computational Complexity of Long Contexts

Supporting a 1-million-token context means attention computation and memory usage grow quadratically. To address this, the model family typically employs a combination of techniques:

• Grouped-Query Attention (GQA) or Multi-head Latent Attention (MLA): These reduce the number of Key/Value heads or compress the latent space dimension, significantly cutting KV cache memory usage.
• Alternating Sliding-Window and Global Attention: Efficient local attention is computed within windows, with periodic global attention layers inserted to preserve long-range dependencies.
• RoPE Positional Encoding Extrapolation and Interpolation: Ensures the model maintains positional awareness for context windows beyond its training length.

These architectural traits directly influence the design of the inference engine—KV cache management policies, attention operator implementations, and positional encoding computation paths all require targeted adaptation.

Inference Behavior Changes from Post-Training and Reinforcement Learning

Models that have undergone extensive post-training and reinforcement learning often exhibit longer chain-of-thought reasoning and more complex tool-calling behavior during inference. For example, in agent tasks, the model may need to execute dozens of sequential steps, each involving reasoning, decision-making, and interaction with external tools. This places additional requirements on the inference framework:

• Dynamic Sequence Length Management: Different requests have vastly different generation lengths, requiring efficient batching and scheduling.
• Multi-Turn Conversation State Maintenance: In agent scenarios, KV cache must be reused across turns, imposing stricter memory management requirements.
• Structured Output Parsing: Tool calling requires the model to output specific formats (e.g., JSON), meaning the inference engine must support constrained decoding.

Key Technical Approaches for Inference Adaptation

Operator Migration and Optimization in the Software Stack

A core task in adapting a model to a domestic AI accelerator platform is migrating the key inference operators from general implementations to the platform’s dedicated acceleration libraries. Typical work includes:

1. Attention Operator Adaptation: The attention variants used by this model family (such as MLA) require specialized operator implementations. The adaptation must fully account for hardware characteristics, such as tiling strategies of matrix compute units and shared memory allocation.
2. Normalization and Activation Functions: Efficient implementations of operators like RMSNorm and SwiGLU.
3. Quantization Operators: If INT8/INT4 quantized inference is used, the corresponding quantization and dequantization operators are needed.
4. Communication Operators: In multi-card parallel scenarios, the performance of collective communication operations—All-Reduce, All-Gather, Reduce-Scatter—directly determines overall inference throughput.

Memory Management and KV Cache Optimization

In ultra-large-scale model inference, the KV cache often occupies a massive amount of memory. With a 1-million-token context, even with GQA compression, the KV cache can still reach hundreds of gigabytes. Optimization strategies include:

• Paged Attention: Manages the KV cache in pages, supporting non-contiguous memory allocation to reduce fragmentation.
• KV Cache Quantization: Stores keys and values in the cache at lower precision.
• Prefix Cache Reuse: For requests sharing the same system prompt, reuses previously computed KV cache entries.

Parallel Strategy Configuration and Tuning

In actual deployment, the optimal parallel configuration depends on hardware topology, including inter-card interconnect bandwidth and NUMA structure. A typical configuration flow is illustrated below:

# Example configuration: combined parallel strategies
parallel_config = {
    "tensor_parallel_size": 8,      # Tensor parallelism degree
    "pipeline_parallel_size": 4,    # Pipeline parallelism degree
    "expert_parallel_size": 4,      # Expert parallelism degree
    "enable_paged_attention": True, # Enable paged attention
    "kv_cache_dtype": "int8",       # KV cache quantization precision
    "max_model_len": 1000000,       # Maximum context length
}

The tuning objective is to maximize throughput while keeping time-to-first-token within acceptable bounds, subject to memory constraints. Key tuning dimensions include batch size, parallelism degree combinations, KV cache allocation ratio, and scheduling strategies such as continuous batching.

Graph Optimization and Operator Fusion in the Inference Engine

To improve inference efficiency, the computation graph is typically optimized:

• Operator Fusion: Merges multiple consecutive small operators into a single kernel, reducing kernel launch overhead and memory read/write operations.
• Computation Graph Rewriting: Eliminates redundant computations, such as merging multiple matrix multiplications.
• Dynamic Shape Support: In ultra-long-context scenarios, sequence lengths change dynamically, so the inference engine must support dynamic-shape compilation and execution.

Adaptation Verification and Performance Considerations

After completing the adaptation, correctness and performance must be verified across several dimensions:

1. Precision Alignment: Compare outputs before and after adaptation on standard evaluation sets to ensure numerical accuracy remains within acceptable bounds.
2. Throughput Testing: Measure tokens per second across different batch sizes and sequence lengths.
3. Latency Testing: Measure time-to-first-token (TTFT) and time-per-output-token (TPOT).
4. Long-Context Stability: Verify that inference remains stable near the maximum context length, with no memory overflow or sudden performance degradation.

For reference, on public benchmarks, this model family performs strongly on code and agent evaluations—scoring 65.7% on Terminal-Bench and 61.2% on a long-horizon code benchmark, a significant improvement over the previous generation. Reliably reproducing these results depends on the completeness and optimization quality of the inference adaptation.

Conclusion and Outlook

Adapting a 3-trillion-parameter model to domestic AI accelerators is a systematic engineering effort involving model architecture analysis, operator migration, memory management, parallel strategy tuning, and inference engine optimization. The core challenge is to efficiently support the loading and inference of ultra-large-scale parameters given limited per-card memory and interconnect bandwidth.

Looking ahead, several trends will shape inference for ultra-large-scale models:

• More Aggressive Quantization: Approaches such as FP8 or even lower precision to further reduce memory and bandwidth pressure.
• Heterogeneous Computing: Assigning different operators to the most suitable compute units based on their characteristics.
• Adaptive Parallelism: Dynamically adjusting parallel strategies and batching based on request patterns.
• Hardware-Software Co-Design: Deep collaboration between inference engines and chip architectures, including custom hardware acceleration paths for core operators like attention.

For developers and enterprises, understanding these technical pathways helps evaluate the adaptation capability of AI compute platforms and provides a reference for planning ultra-large-scale model deployments. As the domestic AI compute software stack continues to mature, the barrier to deploying trillion-parameter and even larger models will keep declining.