Running Ultra-Large-Scale Models on Domestic AI Accelerator Cards: A Day-0 Inference Adaptation Guide

1. Background: Why Ultra-Large-Scale Model Deployment Depends on Day-0 Adaptation of Domestic Compute

As models scale from hundreds of billions to trillions of parameters, they place extreme demands on the underlying compute infrastructure. Several open-source communities have recently released models at the 3-trillion-parameter level with support for million-token contexts, delivering strong results in code generation, multimodal content creation, financial analysis, and long-horizon agent workloads. Bringing these models to production requires not only training clusters but also rapid adaptation of inference hardware and software stacks.

For domestic AI accelerator cards—China’s homegrown AI chips—the ability to provide inference support on the day a model is open-sourced, known as Day-0 adaptation, has become a core indicator of software ecosystem maturity. This article presents a reusable adaptation methodology across three dimensions: model architecture analysis, inference path migration, and software stack optimization.

2. Core Technical Characteristics and Memory Challenges

2.1 Parameter Scale and Memory Pressure: It’s Not Just 6 TB

At FP16 precision, 3 trillion parameters occupy roughly 6 TB in static weights—but that is far from the total memory required for inference. Runtime also needs headroom for the KV cache, activations, and temporary buffers, and peak memory demand is often more than 30% higher. Multi-card parallelism and model sharding are therefore essential:

• Tensor parallelism: splits weight matrices along their dimensions across multiple cards, reducing the load on each card;
• Pipeline parallelism: splits the model by layers, with different cards handling different layer segments;
• Expert parallelism: for MoE architectures, distributes expert networks across devices—extra care is needed for activation memory fluctuations caused by dynamic routing.

When adapting a model, the sharding granularity should be chosen based on the accelerator’s memory capacity, interconnect bandwidth, and compute unit characteristics.

2.2 The Computational Cost of Million-Token Contexts

Million-token contexts cause attention computation to grow quadratically with sequence length. Common optimizations include sparse attention (sliding windows or combined global/local patterns), KV cache quantization and compression, and chunked prefill. These techniques deliver their expected benefits only when they work closely with the underlying hardware’s matrix compute units and memory hierarchy.

2.3 How Post-Training Raises Inference Complexity

These models undergo large-scale post-training and reinforcement learning, delivering clear gains over previous generations on code and agent benchmarks. As a result, inference engines must handle more complex tool calling, multi-turn planning, and long-range sequence dependencies, which raises the bar for scheduling and memory management.

3. Adaptation Methodology: A Four-Step Engineering Path

3.1 Engineering Prerequisites for Day-0 Adaptation

Day-0 adaptation means completing inference support for the target hardware on the day the model is open-sourced. Achieving this requires a unified operator interface and memory management abstraction layer, automated graph optimization and operator fusion capabilities, and reusable parallel strategy templates.

3.2 Key Steps in Inference Path Migration

Step 1: Model structure parsing and operator extraction

Parse the model’s computation graph and identify custom operators, special attention patterns (such as GQA combined with RoPE), and normalization layers to prepare for hardware-friendly refactoring.

Step 2: Operator mapping and performance tuning

Map operators onto the accelerator’s compute units. The following pseudocode uses a PyTorch-style API; in practice, it must be adapted to the specific domestic accelerator SDK:

# Pseudocode: attention computation mapping (PyTorch-style; adapt to your accelerator's SDK)
def scaled_dot_product_attention(Q, K, V, mask=None):
    # On the accelerator, this operation compiles into fused kernels
    # covering matmul, scaling, softmax, masking, and a second matmul
    scores = matmul(Q, K.transpose(-2, -1)) / sqrt(d_k)
    if mask is not None:
        scores = scores + mask
    attn = softmax(scores, dim=-1)
    return matmul(attn, V)

Fused operators are decomposed into hardware-executable micro-instruction sequences, with pipelining used to hide memory access latency.

Step 3: Parallel strategy configuration and communication optimization

Based on the accelerator’s interconnect topology, configure a hybrid scheme combining tensor, pipeline, and expert parallelism. At the communication level, reduce latency through low-precision communication and quantized compression, overlapping communication with computation, and topology-aware scheduling.

Step 4: Precision alignment and stability validation

After mapping is complete, compare outputs layer by layer, requiring a cosine similarity above 0.999. Also run long-sequence stability regression tests to ensure KV cache management behaves correctly.

3.3 Layered Software Stack Support

Layer	Function	Adaptation Focus
Driver layer	Hardware resource management	Correct multi-card enumeration and topology discovery
Runtime layer	Memory allocation, stream scheduling	Support for large-page memory and asynchronous copies
Communication library	Collective communication primitives	Adaptation of AllReduce, AllGather, and similar operations
Operator library	Deep learning operators	Coverage of attention, normalization, activation, and more
Framework adaptation layer	Integration with mainstream training/inference frameworks	Plugin-style backends

Note: The software stack layers described here are a generic representation. Implementations vary considerably across domestic accelerator products, so always refer to the official documentation.

4. Performance Validation and Typical Applications

4.1 Benchmark Dimensions

Once adaptation is complete, validate performance across four dimensions: throughput (tokens per unit of time), first-token latency, memory stability on long sequences, and consistency across multi-turn conversations.

4.2 Typical Use Cases

• Intelligent coding assistants: understanding entire codebases within long contexts to deliver cross-file completion;
• 3D content generation: multimodal understanding driving 3D scene construction;
• Financial research and analysis: extracting key metrics from lengthy research reports;
• Long-horizon agent tasks: multi-step planning and tool calling.

5. Engineering Practices and Selection Recommendations

1. Verify software stack compatibility first: confirm that the inference framework, communication library, and operator library support the target model architecture;
2. Plan the parallel strategy carefully: choose a hybrid parallel scheme based on cluster size and interconnect bandwidth;
3. Prioritize memory management: use KV cache quantization and paged attention to reduce peak memory usage;
4. Build a precision regression system: continuously compare outputs to catch deviations early;
5. Focus on long-sequence performance: optimize prefill efficiency specifically for million-token contexts.

When selecting hardware, also consider driver version compatibility, the progress of mainstream framework adaptation, and community activity—all of which directly affect long-term maintenance costs.

6. Conclusion

Adapting ultra-large-scale models to domestic AI accelerator cards is a systems engineering effort spanning hardware, software, and algorithms. With layered software stack abstraction, automated operator fusion, and flexible parallel strategies, domestic accelerator cards can complete Day-0 inference adaptation on the day a model is open-sourced, providing the compute foundation for trillion-parameter model deployment. As model sizes keep growing, hardware-software co-design will be the key path to unlocking the full potential of domestic AI compute.