Adapting LLMs from 100B to Trillion-Scale Parameters: Key Technical Paths

As AI enters the era of trillion-parameter models, efficient deployment and stable operation across diverse hardware ecosystems have become core engineering challenges for enterprises and developers. Recently, a domestic AI accelerator completed Day-0 adaptation of a 3-trillion-parameter open-source model. This milestone not only demonstrates the rapid maturation of the domestic computing ecosystem but also offers a valuable reference for deploying ultra-large-scale models in production. This article uses that case as a starting point to examine the key technical steps, common pain points, and reusable solutions involved in migrating models from the 100-billion to the trillion-parameter scale.

1. New Computational Challenges of Trillion-Parameter Models

When model size grows from hundreds of billions to trillions of parameters, the technical challenges are not simply linear — they represent a qualitative shift. The recently adapted K-series model, for example, has 3 trillion parameters and supports a context window of up to 1 million tokens. At this scale, even storing model weights in half precision (FP16/BF16) requires nearly 6 TB for a single copy. When KV cache, intermediate activations, and optimizer states are factored in, memory requirements easily exceed tens of terabytes.

1.1 The Memory Wall: Dual Bottlenecks of Capacity and Bandwidth

Traditional single-GPU or even single-node deployment strategies completely break down for trillion-parameter models. A typical 8-GPU server with 80 GB of high-bandwidth memory per card provides only 640 GB total — far below what the model requires. This forces the adoption of multi-node, multi-GPU distributed inference architectures.

Even more challenging is the large gap between inter-GPU communication bandwidth (such as NVLink or InfiniBand) and memory bandwidth. This gap makes tensor parallelism and data parallelism strategies significantly more complex. If parallelism is not carefully planned, communication overhead can completely negate computational speedups, causing training and inference efficiency to plummet.

1.2 Computational Efficiency: From Compute-Bound to Memory-Bound

During inference, a trillion-parameter model performs only one forward pass per generated token, but the amount of data involved is enormous. In MLP layers, for example, weight matrix dimensions can reach tens of thousands by tens of thousands. While the matrix multiplications demand substantial compute, modern accelerators — including domestic AI accelerators — generally provide sufficient FP16/BF16 throughput. The real bottleneck lies in loading and moving weights. Every matrix operation requires reading massive weight data from HBM, placing extreme demands on memory bandwidth far beyond what typical image recognition or natural language understanding models require. Therefore, reducing memory access through operator fusion, operator replacement, and computation graph optimization becomes a top priority in adaptation work.

2. Software Stack Adaptation: The Bridge Between Algorithm and Hardware

Achieving efficient execution of any model on specific hardware requires a mature, complete, and highly compatible software stack. The adaptation case discussed here, based on the MUSA software stack (a CUDA-like unified system architecture), illustrates the critical role a software stack plays in heterogeneous computing.

2.1 Operator-Level Mapping and Optimization

High-performance computing is fundamentally about operators. Third-party open-source models are typically developed using widely adopted deep learning frameworks such as PyTorch, composed of many standard operators including matrix multiplication, normalization, activation functions, and attention mechanisms. The first step in adaptation is mapping these operators to the target hardware instruction set.

• Standard operator adaptation: For standard operators, the MUSA software stack provides a rich operator library that can be called directly, enabling zero-cost migration.
• Custom operator optimization: FlashAttention in the Transformer architecture, as well as routing and load balancing in MoE (Mixture of Experts) modules, are often the most heavily customized components. These operators cannot be directly mapped to general-purpose libraries and require hand-written kernels optimized for the hardware’s SRAM capacity and compute unit layout. For example, implementing FlashAttention requires carefully controlling how data is chunked and moved between SRAM and HBM to maximize data reuse and minimize HBM bandwidth consumption.

2.2 Graph Compilation and Runtime Scheduling

Beyond individual operator efficiency, the execution efficiency of the entire computation graph is equally important. The MUSA software stack uses graph compilation techniques to deeply optimize the model’s computation graph. Through operator fusion (merging multiple small operators into larger fused operators), memory pooling (reusing intermediate computation buffers to reduce memory allocation overhead), and launch latency hiding, it effectively improves end-to-end graph execution efficiency. At the runtime scheduling level, intelligent task queue management and multi-stream concurrency maximize the utilization of all compute resources on the accelerator. This ensures that during long-sequence generation tasks (such as 1-million-token contexts), compute resources remain highly utilized and pipeline bubbles caused by synchronization waits are avoided.

3. Critical Path Migration: Deep Refactoring of the Inference Pipeline

Based on the model’s architecture and computational characteristics, the adaptation team migrated the critical inference path and enabled stable operation on specific accelerator models such as the MTT S5000. This process can be viewed as a surgical, precision-driven refactoring effort.

3.1 Dynamic Shape Handling and Memory Management

The defining characteristic of LLM inference is the dynamic variation in sequence length, which can range from dozens to a million tokens. This causes tensor shapes in the computation graph to change frequently. Static graph compilation often cannot handle dynamic shapes. During adaptation, the software stack introduced a dynamic shape compiler that can adjust kernel launch parameters and memory allocation strategies at runtime without recompiling the entire graph.

To address memory fragmentation during LLM inference, the team adopted a virtual memory pool-based management scheme. This approach reserves large blocks of contiguous virtual address space, uses page table mapping, and allocates physical memory on demand. It effectively eliminates the issue of fluctuating memory usage during long-sequence inference and improves memory utilization to above 90%.

3.2 Deep Integration of Distributed Parallelism Strategies

For trillion-parameter models, a single parallelism strategy is far from sufficient. A combination of multiple strategies is required.

• Tensor parallelism: Splits the matrix operations of a single Transformer layer across multiple devices by rows or columns to reduce per-operation latency, but introduces substantial communication overhead.
• Pipeline parallelism: Assigns different layers to different devices to reduce communication volume, but increases pipeline bubbles.
• Data parallelism: Handles large batch sizes by distributing data across devices.

The adaptation team used detailed performance modeling to determine the optimal parallelism configuration for the current hardware topology. For example, for MoE layers with 16 experts, the team adopted expert parallelism, splitting experts into two groups deployed on two separate machines. A global routing mechanism ensured load balancing, keeping compute load differences across accelerators below 5%. Additionally, communication-computation overlap techniques executed All-to-All communication concurrently with the current layer’s computation, reducing the impact of MoE layer communication on overall performance by nearly half.

3.3 Extreme Optimization for Long-Sequence Inference

Supporting a 1-million-token context is a major highlight of this model — and a significant test for the inference engine. Long-sequence inference faces two primary constraints: the O(n²) complexity of attention mechanisms causing explosive compute growth, and the massive memory footprint of the KV cache.

For the first constraint, the adaptation adopted an attention mechanism optimization with log-linear complexity. The approach decomposes long-sequence attention into a combination of sparse attention and local window attention, substantially reducing computational load while preserving accuracy.

For the second constraint, the team introduced PagedAttention-style memory management, dividing the KV cache into fixed-size physical blocks allocated on demand. This completely eliminates memory fragmentation and increases the number of concurrent requests supported under the same memory capacity by several times. At the same time, recognizing that different attention heads across layers have varying importance, the team implemented KV cache quantization (such as compressing from FP16 to INT8), reducing KV cache memory usage by nearly 50% and reserving sufficient space for ultra-long sequences.

4. Summary of Technical Practice Paths and Recommendations

From the adaptation case above, a general technical path for trillion-parameter model adaptation can be distilled:

4.1 Assessment and Planning

Before running the model, use performance analysis tools to quantitatively evaluate hardware compute capacity, memory bandwidth, and communication bandwidth. Determine the maximum model size the current hardware can support and select an appropriate combination of parallelism strategies.

4.2 Operator Porting and Validation

Prioritize automatic migration tools to import approximately 90% of standard operators. For bottleneck operators such as FlashAttention and MoE modules, develop hand-written kernels. Establish dual-precision validation at both the operator level and model level to ensure output differences after migration remain within acceptable error ranges.

4.3 Guided Inference Optimization

Combine a dynamic shape compiler with memory pooling to build an inference engine with on-demand compilation capabilities. Gradually increase input sequence lengths (from 1K to 100K to 1M) to progressively evaluate system stability and identify problems such as memory spikes and kernel execution timeouts under long sequences.

4.4 Continuous Iteration

After model deployment, set up monitoring dashboards to continuously track key metrics such as compute utilization, memory utilization, and time-to-first-token. Based on historical runtime data, dynamically fine-tune parallelism configurations and operator parameters to achieve better production performance.

5. Conclusion and Outlook

Adapting trillion-parameter models is a highly demanding systems engineering effort. It tests not only hardware compute capacity but also the maturity of the software stack and the richness of the toolchain. This successful case demonstrates that with advanced software architecture, refined operator optimization, and intelligent memory management, domestic AI accelerators are fully capable of supporting the most advanced large-scale models. Looking ahead, as heterogeneous computing architectures continue to evolve, training and inference of models with hundreds of trillions of parameters on clusters of tens of thousands of accelerators will likely become feasible. For developers, focusing on engineering-level adaptation methodologies and concrete optimization techniques is more important than the hardware itself, because only by tightly integrating algorithms and hardware can the full potential of large models be unlocked.