Engineering a 3-Trillion-Parameter Model: Inference Deployment and Performance Tuning at Scale

1. Background: Crossing the 3-Trillion-Parameter Threshold

As large language models continue to scale, 3-trillion-parameter dense or Mixture-of-Experts (MoE) models are no longer confined to research papers—they are entering real deployment. These models not only carry an enormous parameter count but also come with ultra-long context windows (e.g., million-token spans), behavioral complexity introduced by multi-stage reinforcement learning post-training, and intensive computational demands for code generation, multi-step reasoning, and long-horizon agent tasks. For AI infrastructure, this means general-purpose GPU clusters alone can hardly balance cost, energy efficiency, and stability. The co-adaptation of specialized AI accelerator cards with domestic software stacks is becoming a central issue for large-model deployment.

Against this backdrop, a domestic AI chip vendor recently announced that its AI training-and-inference accelerator card and proprietary software stack had completed Day-0 adaptation for a newly open-sourced 3-trillion-parameter model, supporting stable inference on standard server nodes. This milestone marks a substantive step forward for domestic AI compute in handling ultra-large-scale models. Using this adaptation as a case study, this article systematically examines the engineering path for deploying very large model inference, exploring the nuanced relationship between hardware characteristics, software stack optimization, and model architecture matching.

2. Model Characteristics: Three Challenges of 3 Trillion Parameters

To understand the complexity of the adaptation work, one must first analyze the target model’s computational profile. Taking the open-sourced model in this case as an example, it exhibits three notable characteristics:

First, the tension between sparse activation and dense storage. The model uses an MoE architecture. Although the total parameter count reaches 3 trillion, only a subset of expert networks is activated during a single forward pass. This design theoretically reduces per-token computation, but all expert weights must still reside in VRAM or memory, creating extreme pressure on storage bandwidth and capacity. The key to adaptation lies in leveraging hardware features to achieve an efficient data path for “on-demand expert scheduling.”

Second, the attention computation bottleneck of million-token contexts. Supporting a 1-million-token context window means the computational complexity of attention mechanisms grows quadratically with sequence length. Even with approximate methods such as sparse attention and sliding windows, KV Cache storage capacity far exceeds that of conventional models. This demands fundamental adjustments to VRAM management, operator fusion, and communication scheduling in the inference engine.

Third, the stringent stability requirements of long-horizon tasks. The model’s strong performance in code generation, financial research, and long-horizon agent tasks depends on hundreds or even thousands of consecutive inference steps. Any numerical anomaly, VRAM overflow, or operator execution deviation at any step can cause the entire task to fail. Therefore, adaptation must ensure not only single-step performance but also multi-step inference consistency in distributed environments.

3. Hardware-Software Co-Design: The Critical Path to Day-0 Adaptation

Day-0 adaptation means completing hardware support on the same day a model is open-sourced. This requires hardware vendors to engage deeply during the model’s development phase, customizing the software stack in advance around the model architecture and computational characteristics. In this adaptation, the core work centered on three main paths:

3.1 Operator Migration and Rewriting for Critical Inference Paths

The inference pipeline for 3 trillion parameters contains numerous custom operators, such as Grouped GEMM, expert parallel routing, and long-sequence FlashAttention variants. General-purpose compute libraries often cannot efficiently support these operators directly. The adaptation team therefore hand-optimized hot operators in the inference computation graph based on the MUSA programming model. Specific strategies included:

• Operator fusion: Merging adjacent operations such as “activation + matrix multiplication + normalization” into a single kernel to reduce VRAM read/write cycles;
• Tensor memory layout adaptation: Rearranging weight tensor data layouts (e.g., NC1HWC0 vs. NCHW) based on the hardware’s local memory bank conflict characteristics to achieve conflict-free memory access;
• Expert parallel load balancing: Designing a dynamic routing scheduler for the uneven token distribution across experts in MoE models, implementing fine-grained task stealing at the streaming multiprocessor (SM) level.

3.2 Squeezing Maximum Capacity and Bandwidth from VRAM

If stored in FP16 precision, 3 trillion parameters would require approximately 6 TB of VRAM for weights alone—far exceeding single-card capacity. The current approach uses a hybrid parallelism strategy of “multi-card tensor parallelism + expert parallelism + data parallelism,” with two levels of VRAM management optimization:

• Hierarchical weight residency: Splitting the model by layer into “hot weights” (frequently accessed attention layers and shared experts) and “cold weights” (infrequently accessed domain experts). Cold weights are stored in compressed formats (e.g., INT8 or FP8) in high-bandwidth memory, while hot weights reside at native precision. On-demand decompression at runtime balances precision and capacity;
• Paged KV Cache management: Borrowing the paging concept from operating system virtual memory, KV Cache is divided into fixed-size page blocks that are allocated and reclaimed on demand, avoiding fragmentation waste during long-context inference. In practice, this reduced redundant VRAM usage by approximately 40%.

3.3 End-to-End Software Stack Co-Optimization

The MUSA software stack served as the “glue layer” in this adaptation, with its core role manifesting in three dimensions:

• Compiler level: Using MLIR dialects to describe the model computation graph and performing joint optimization of “automatic computation graph partitioning + communication insertion,” automatically identifying parallelizable tensor-splitting dimensions and generating efficient collective communication primitives across PCIe or NVLink;
• Runtime level: Providing a task-dependency-aware stream scheduler that makes dependencies between different computation streams explicit, achieving over 85% multi-card compute overlap;
• Debugging and monitoring level: For long-horizon inference scenarios, providing operator-level numerical consistency verification tools that can compare outputs operator by operator in multi-card environments, quickly locating error drift caused by floating-point accumulation order.

4. Performance Validation and Tuning: From “Running” to “Running Well”

The ultimate goal of adaptation is stable model operation, but the real engineering challenge is meeting performance targets. Published benchmark data shows that in code generation (Terminal-Bench 4.0) and long-horizon agent tasks (Kimi Code Bench 3.0 Long-horizon), the model’s scores are on par with or close to top closed-source models. Behind this, the adaptation team went through multiple rounds of system-level tuning:

Round 1: Eliminating I/O bottlenecks. In the initial version, on-demand expert weight loading saturated PCIe bandwidth, driving per-token latency as high as 2.3 seconds. The optimization introduced an “expert prefetch queue + weight cache hotness statistics” to predict the expert set needed in the next few steps and initiate DMA transfers ahead of time, ultimately compressing I/O wait time to under 15% of total latency.

Round 2: Fine-tuning load balancing. Observations revealed that some experts became performance hotspots due to high-frequency token assignment, while others were underutilized. To address this, an online routing corrector based on reinforcement learning was designed. Under a KL divergence constraint to preserve model accuracy, it dynamically shifts token-to-expert mappings, reducing the coefficient of variation in per-expert computation time from 0.45 to 0.12.

Round 3: Long-sequence sparse attention tuning. For million-token contexts, the default implementation triggered VRAM swapping once sequence length exceeded 512K. By changing the sparse attention pattern from a “fixed window” to a three-channel mode of “window + global tokens + random tokens,” combined with block-level kernels at the operator level, the model maintained a generation speed above 12 tokens per second at 1M sequence length, meeting the basic requirements of interactive tasks.

5. A General Methodology for Ultra-Large Model Adaptation

The adaptation experience can be abstracted into a replicable methodology applicable to models ranging from hundred-billion to trillion parameters:

1. Early architectural matching: Hardware vendors must provide a “simulation runtime environment” (based on model architecture graphs rather than actual weights) during the pre-training phase, allowing algorithm teams to expose potential VRAM bottlenecks and communication hotspots in advance;
2. Runtime elastic adaptation: The software stack must support “hot-swappable model structures,” meaning expert parallelism or KV Cache paging strategies can be dynamically adjusted mid-inference to accommodate dynamic batch sizes and variable-length inputs;
3. Benchmark-driven optimization: Designing micro-benchmark suites for specific tasks such as code, reasoning, and long-horizon agents, replacing general-purpose performance metrics (e.g., raw TFLOPS) with direct measurement of end-to-end task success rates and latency;
4. Ecosystem compatibility and standards compliance: The adaptation process should maximize compatibility with mainstream model formats (e.g., Safetensors) and inference interfaces (e.g., vLLM’s OpenAI-compatible API) to reduce user migration costs.

6. Future Outlook: From Adaptation to Native Optimization

Day-0 adaptation solves the “can it run” problem, but a significant gap remains before reaching “peak efficiency.” Next-step evolution directions include:

• Architecture-native co-design: The next-generation AI accelerator card may integrate expert routing modules or sparse attention compute units directly at the hardware level, fundamentally eliminating software emulation overhead;
• Model-hardware joint distillation: Customizing lightweight expert structures for specific hardware platforms to further compress VRAM footprint without losing performance;
• Heterogeneous computing extension: Offloading certain non-critical operators (e.g., tokenization, regex matching) to CPU or NPU sides, freeing accelerator card compute resources for core tensor operations.

It is foreseeable that as model parameter counts march toward 10 trillion, the boundary between hardware and software will become increasingly blurred. Only deep co-optimization can support the deployment needs of next-generation AI applications. The successful adaptation of this 3-trillion-parameter model undoubtedly provides a highly valuable engineering reference for this trend.