Adapting Domestic GPUs for Ultra-Large-Scale Models: From Model Characteristics to Inference Path Migration

1. Background: Ultra-Large Models are Reshaping Inference Engineering

With the maturation of Mixture of Experts (MoE) architectures and ultra-long context capabilities, model parameter scales have evolved from hundreds of billions to trillions. A recently released series of models from an open-source community debuted with several trillion parameters and a million-token context window, showing significant improvements in code generation, 3D content creation, financial research, knowledge QA, and long-horizon Agent tasks.

The engineering significance of such models lies not merely in having “more parameters,” but in the structural new requirements they impose on inference systems:

• Sparse activation brings dynamic computation graphs: In MoE architectures, a single forward pass only activates a subset of experts. Routing results depend on the input, causing computation and memory access patterns to vary per request. Traditional static graph optimization methods are difficult to apply directly.
• Ultra-long context amplifies KV Cache pressure: A million-token context means the memory footprint of the KV Cache may exceed that of the model weights themselves, posing simultaneous challenges to memory capacity, bandwidth, and management strategies.
• Post-training and reinforcement learning introduce more complex inference behaviors: Long-horizon Agent tasks require models to maintain state consistency across multi-turn tool calls. Inference services must support more flexible scheduling and interruption recovery.

Therefore, completing the “Day-0 adaptation” for a trillion-parameter model is fundamentally a systematic engineering effort covering operators, memory, scheduling, and precision, rather than a simple model format conversion.

2. Methods for Decomposing Model Computational Characteristics

Before diving into adaptation, the first step is to “unpack” the model. Engineering teams typically establish a computational profile from the following dimensions:

2.1 Operator Inventory and Hotspot Distribution

Catalog the operator types appearing in the model and their call frequencies, focusing on:

• Attention-related variant operators (e.g., attention with fused positional encoding, Grouped Query Attention);
• Gather/scatter operators related to MoE routing and expert dispatch;
• Opportunities for fusing normalization and activation functions.

A practical approach is to first run the reference implementation, then use profiling tools to output the time-consumption ratio at the operator level to form a “hotspot ranking list.” Subsequent optimization resources should be prioritized for the top 20% of operators.

2.2 Memory and Bandwidth Estimation

Estimate peak memory in the following order:

Weight Memory ≈ Parameter Count × Bytes per Parameter (e.g., 2 bytes for FP16)
KV Cache ≈ 2 × Number of Layers × Sequence Length × Hidden Dimension × Bytes per Element × Batch Size
Activation Memory ≈ Proportional to Batch Size, Sequence Length, and Intermediate Layer Dimension

For million-token context scenarios, the KV Cache often becomes the dominant factor. It is necessary to plan paging management, quantization compression, or tiered storage strategies in advance.

2.3 Feasibility Assessment of Parallelism Strategies

Trillion-parameter models usually cannot run on a single GPU and require a combination of Tensor Parallelism (TP), Pipeline Parallelism (PP), or Expert Parallelism (EP). Before adaptation, it is necessary to clarify:

• Whether a single GPU’s memory is sufficient to hold a single expert shard;
• Whether the communication domain size matches the interconnect topology;
• Whether layers with cross-node communication will become bottlenecks.

3. Key Stages of Inference Path Migration

Based on the aforementioned computational characteristics, the adaptation work can be decomposed into four key stages.

3.1 Operator Mapping and Fusion

Map the operators in the model one by one to the operator library provided by the target software stack. For missing operators, prioritize implementing them using combinations of basic operators, then evaluate whether writing custom operators is necessary. Common optimizations include:

• Fusing LayerNorm with residual addition to reduce one memory read/write;
• Merging softmax and masking operations in attention to reduce intermediate tensor residency;
• Applying grouped batching to MoE expert feed-forward passes to improve the utilization of small matrix multiplications.

3.2 Memory Management and KV Cache Optimization

For long-context scenarios, it is recommended to adopt paged KV Cache management: dividing the cache into fixed-size blocks, allocating and reclaiming them on demand to avoid memory fragmentation caused by inconsistent sequence lengths. Additionally, low-precision caching (e.g., FP8) can be introduced on layers where precision is acceptable to reduce bandwidth usage.

3.3 Scheduling and Batching Strategies

Continuous batching is a common method to improve throughput, but under MoE models, expert load balancing requires extra consideration: different requests activate different sets of experts. If batching is simply based on request count, some experts may be overloaded while others remain idle. In practice, batches can be dynamically split based on expert capacity, and request prioritization can be introduced at the scheduling layer to guarantee the response latency of long-horizon Agent tasks.

3.4 Precision Alignment and Numerical Stability

During migration, a layer-by-layer numerical comparison mechanism must be established: using the reference implementation as a baseline, compare the maximum absolute error and relative error of each layer’s output to locate the source of deviation. For the top-k selection in MoE routing, floating-point errors may lead to inconsistent expert choices. It is necessary to ensure reproducibility by increasing the precision of routing calculations or introducing deterministic rounding strategies.

4. Validation and Performance Evaluation Methods

The criteria for a completed adaptation should be “stable execution + reproducible metrics.” It is recommended to establish a three-tier validation system:

1. Functional Correctness: Under a fixed random seed, compare the output consistency of the model before and after migration on standard evaluation sets. For tasks like code generation and long-horizon Agents, measure task success rates rather than simple text similarity.
2. Performance Benchmarks: Measure Time To First Token (TTFT), Time Per Output Token (TPOT), and throughput separately. Plot curves under different batch sizes and sequence lengths to avoid reporting only the optimal points.
3. Stability and Soak Testing: Run continuously for dozens of hours, observing memory growth trends, communication timeouts, and error rates to verify robustness under combined scenarios of long context and high concurrency.

According to public information, similar models have achieved high scores on code and Agent benchmarks (e.g., approximately 65% on terminal operation benchmarks, 61% on long-horizon code benchmarks). This suggests that adaptation acceptance should also cover evaluations based on such real-world tasks, rather than relying solely on traditional metrics like perplexity.

5. Practical Recommendations and Common Pitfalls

• Do not skip operator hotspot analysis: Applying general optimizations directly often yields limited results. Clarify the time distribution first.
• Beware of the gap between “runs successfully” and “serves reliably”: Running a single request is just the starting point. Batching, scheduling, and memory reclamation are the keys to productionization.
• Expose precision issues early: Establish a layer-by-layer comparison process early in the adaptation phase to avoid discovering deviations only during large-scale evaluations later.
• Design test cases specifically for long contexts: The memory behavior of million-token contexts differs vastly from short sequences and requires independent validation.

Conclusion

Adapting trillion-parameter models is a systematic engineering effort that starts with computational characteristic analysis, follows the main thread of inference path migration, and concludes with multi-layer validation. For teams looking to host such models on domestic GPU platforms, it is recommended to follow the path of “profile first, map second, tune third, validate last.” Testing every assumption with data is the only way to maintain engineering control amid the rapid iteration pace of models.