Technical Path for Adapting Domestic GPUs to Ultra-Large-Scale Models

1. Background: Deployment Challenges of Ultra-Large-Scale Models

Recently, an open-source large model featuring a trillion-parameter scale and support for a million-level context length was officially released. After extensive post-training and reinforcement learning, this model has achieved significant improvements across multiple dimensions, including code generation, 3D content creation, financial research, knowledge QA, and long-horizon Agent tasks. In certain code and Agent benchmarks, its performance approaches or even matches that of top-tier closed-source models.

However, a significant engineering gap remains between making such ultra-large-scale models “open-source” and making them “usable.” Ensuring that domestic AI accelerators can complete adaptation on the exact day the model is released (Day-0 adaptation) and run stably on domestic computing power is a core challenge currently facing technical teams. From a technical implementation perspective, this article systematically outlines the key paths and general methodologies involved in this adaptation process.

2. Core Stages of the Adaptation Process

2.1 Model Architecture Parsing and Operator Profiling

Ultra-large-scale models typically adopt a Mixture of Experts (MoE) architecture or a dense parameter architecture. The computation graph during inference contains numerous custom and fused operators. The first step in adaptation is to thoroughly parse the model’s computation graph and identify the following key elements:

• Attention Mechanism Variants: The model supports million-level context, implying that attention computations may involve optimization strategies like sparse attention, sliding window attention, or blockwise attention. During adaptation, it is necessary to verify whether the accelerator natively supports these variants or if equivalent computation must be achieved through operator combinations.
• Normalization and Activation Functions: Different models may utilize non-standard components such as RMSNorm and SwiGLU, which need to be mapped one by one to the accelerator’s operator library.
• Positional Encoding Schemes: In long-context scenarios, variants of Rotary Position Embedding (RoPE) and their frequency scaling strategies must be accurately reproduced. Otherwise, it will lead to positional information disorder during long-text inference.

2.2 Memory Management and Quantization Strategies

Loading the weights of a trillion-parameter model imposes extremely high demands on memory capacity. Even with FP8 or INT8 quantization, model weights still require hundreds of gigabytes of memory space. Therefore, the adaptation process must consider the following strategies:

• Weight Quantization and Shard Loading: Model weights are sharded by layer or by expert, and loaded in a pipeline fashion in accordance with the accelerator’s memory capacity. For MoE architectures, the load balancing of expert routing must also be considered.
• KV Cache Optimization: Million-level context implies that the KV Cache may become a memory bottleneck. Technologies like PagedAttention or KV Cache quantization compression can be employed to reduce cache usage to an acceptable range.
• Activation Recomputation: When memory is insufficient, memory space can be traded off by recomputing certain intermediate activation values. This requires the framework layer to provide fine-grained recomputation strategy configurations.

2.3 Inference Path Migration and Operator Fusion

After completing operator mapping, the next step is to fully migrate the model’s inference path to the target hardware. This process involves:

• Computation Graph Optimization: Fusing multiple consecutive small operators into larger ones to reduce kernel launch overhead and memory access times. For example, fusing LayerNorm with a linear layer, or combining QKV projections in attention.
• Communication and Computation Overlap: In multi-GPU inference scenarios, tensor parallelism or pipeline parallelism introduces cross-card communication. It is necessary to leverage the asynchronous communication capabilities of the accelerator to overlap communication with computation, thereby hiding communication latency.
• Dynamic Shape Support: In long-context scenarios, the input sequence length varies greatly. The inference engine needs to support dynamic shapes to avoid frequent recompilation or memory waste caused by fixed shapes.

# Pseudo-code: Operator fusion logic
def fused_attention_forward(query, key, value, mask):
    # Fuse the three steps of QK^T, softmax, and PV multiplication into a single kernel
    scores = matmul(query, key.transpose(-2, -1))
    scores = scores * scale + mask
    attn_weights = softmax(scores)
    output = matmul(attn_weights, value)
    return output

3. Performance Evaluation and Verification

After adaptation is complete, the model’s performance on domestic accelerators must be verified across multiple dimensions:

3.1 Functional Correctness Verification

• Precision Alignment: Perform layer-by-layer comparisons between the inference output on domestic GPUs and reference implementations (such as CPUs or mainstream international accelerators) to ensure numerical errors are within an acceptable range.
• Long-Context Stability: For million-level context inputs, verify whether the model experiences positional information drift, attention degradation, or memory overflow issues.
• Agent Task Loop Testing: In long-horizon tasks like code generation and tool calling, verify the model’s stability and consistency under multi-turn interactions.

3.2 Performance Benchmarking

Referring to common industry evaluation dimensions, the adaptation results can be measured from the following perspectives:

• Time to First Token (TTFT): Reflects the efficiency of the model loading and prefill stages.
• Throughput: The number of tokens processed per second under fixed concurrency, which directly impacts service costs.
• Long-Context Scalability: Whether the growth curve of latency and memory remains smooth as the context length increases.
• Benchmark Scores: In code and Agent benchmarks, the adapted model’s scores should be close to officially published data. For instance, achieving over 65% in terminal operation benchmarks and over 60% in long-horizon code benchmarks.

4. General Strategies for Software-Hardware Co-Optimization

4.1 The Critical Role of the Software Stack

Domestic accelerators typically come with a comprehensive software stack, including operator libraries, communication libraries, inference engines, and compilers. During adaptation, the software stack needs to provide:

• Operator Coverage: Provide efficient implementations for operators in mainstream models to minimize fallbacks to general-purpose computing.
• Graph Compilation and Optimization: Support automatic computation graph optimization, including operator fusion, memory reuse, and pipeline scheduling.
• Multi-GPU Parallelism Support: Provide primitives for tensor parallelism, pipeline parallelism, and data parallelism to lower the development barrier for distributed inference.

4.2 Fully Leveraging Hardware Characteristics

Different accelerators have distinct features regarding memory bandwidth, interconnect bandwidth, and compute unit configurations. Targeted optimizations should be applied during adaptation:

• High-Bandwidth Memory: Prioritize placing frequently accessed weights and KV Cache in on-chip memory or high-bandwidth memory.
• Dedicated Matrix Compute Units: Map matrix multiply-accumulate operations to dedicated compute units to enhance computational efficiency.
• Inter-GPU Interconnect: In multi-GPU scenarios, utilize high-bandwidth interconnect channels for All-Reduce or All-Gather communications to reduce communication bottlenecks.

5. Conclusion and Future Outlook

Day-0 adaptation of ultra-large-scale models is fundamentally a systematic engineering effort involving model parsing, operator mapping, memory management, inference optimization, and performance verification. Through complete software stacks and hardware co-design, domestic AI accelerators now possess the capability to complete adaptation and run stably on the day a model is released.

For developers and enterprise users, this means that when selecting domestic computing solutions, they can focus more on the following capabilities:

1. Rapid Response to New Models: Whether the engineering system and experience for Day-0 adaptation are in place.
2. Long-Context Support Capability: Whether memory management and attention optimizations are sufficient to support million-level context.
3. Ecosystem Compatibility: Whether the software stack is compatible with mainstream inference frameworks and model formats to reduce migration costs.
4. Performance Predictability: Whether public benchmark data and tuning guides are available for reference.

As model sizes continue to grow and application scenarios expand, the maturity of the domestic computing ecosystem will become a critical factor in determining whether large models can be deployed efficiently. By continuously optimizing software-hardware co-design capabilities, domestic accelerators are poised to occupy a significant position in the field of ultra-large-scale model inference.