Trillion-Parameter LLM Inference Adaptation: From Migration to Stable Operation

1. Background: The New Normal of Ultra-Large Model Deployment

As Large Language Model (LLM) parameter scales expand from hundreds of billions to trillions, the complexity of model deployment has fundamentally changed. Taking a recently open-sourced 3-trillion-parameter sparse expert model as an example, it not only sets new records in parameter count but also introduces architectural innovations such as large-scale expert parallelism, cross-node communication, and long-context attention. Deploying such models for inference is no longer as simple as “loading weights and running a forward pass.” It has evolved into a systematic engineering effort encompassing compute selection, software stack adaptation, communication topology optimization, VRAM management, and inference scheduling.

For the domestic compute ecosystem, a more pressing question arises: against the backdrop of mainstream GPU supply chain uncertainties, how can domestic AI chips efficiently handle the inference workloads of these top-tier LLMs? This challenge goes beyond single-chip peak compute power; it heavily tests the maturity of the software stack, ecosystem compatibility, and the engineering team’s ability to deconstruct the underlying computational patterns of the model.

This article uses the Day-0 adaptation of a 3-trillion-parameter open-source model on a domestic AI chip (hereafter referred to as “the chip”) as a case study. It outlines a complete technical pathway for adapting ultra-large LLMs to domestic hardware and distills reusable engineering experiences.

2. Deconstructing Key Model Computational Characteristics

Before writing any adaptation code, it is crucial to accurately deconstruct the computational profile of this 3-trillion-parameter model. Based on public technical reports and model cards, several characteristics directly impacting the deployment strategy can be identified:

1. Dual Memory and Communication Pressure from Sparse Expert Architecture

The model employs a typical Mixture-of-Experts (MoE) architecture, where activated parameters are far fewer than total parameters. However, inference still requires loading all expert weights into VRAM. A rough estimate shows that 3 trillion parameters in FP16 format require over 6TB of VRAM. This dictates that a single-node, 8-GPU setup (even with 80GB per GPU) is entirely unfeasible, necessitating a hybrid strategy of multi-node tensor parallelism combined with expert parallelism. Crucially, in MoE models, token-level routing distributes each token to different experts, making cross-node communication a primary system bottleneck.

2. Extreme KV Cache Demands from Million-Token Contexts

The model supports a context window of 1 million tokens. During inference, the KV Cache grows linearly with sequence length and is stored independently for each attention head. For a 1-million-token sequence, the KV Cache capacity requirement can reach hundreds of gigabytes, far exceeding the model weights themselves. This means the inference system must introduce an efficient KV Cache management mechanism, including but not limited to PagedAttention-style on-demand allocation, overlapping attention computation with memory access, and dynamic VRAM swapping for long sequences.

3. Inference Mode Variations from RLHF Post-Training

The model underwent large-scale Reinforcement Learning from Human Feedback (RLHF) post-training, incorporating multiple inference modes: generative tasks with short queries and long responses, multi-turn tool-calling Agent tasks, and extended reasoning for complex code generation. Different modes exhibit significant variations in batch size, sequence length distribution, and compute-to-memory access ratios. A “one-size-fits-all” inference engine struggles to perform optimally across all scenarios, requiring support for dynamic batching, continuous batching, and request-feature-based scheduling strategies.

3. Software Stack Adaptation: Bridging the Full Pipeline

Once the model’s computational characteristics are clear, the next step is software stack adaptation. This process is typically evaluated across three layers:

Layer 1: Operator Layer. The model relies on a highly concentrated set of operator types, primarily matrix multiplication (GEMM), attention variants (including FlashAttention for long sequences), MoE routing (including TopK selection and permutation), and activation functions. On this chip, operator adaptation is not a simple CUDA API mapping. It requires reimplementation tailored to the chip’s matrix compute units, SRAM capacity, and memory bandwidth characteristics. Key optimization points in practice include:

• For MoE matrix multiplication, utilizing Grouped GEMM to consolidate computations across experts, reducing kernel launch overhead.
• For million-token sequence attention, adopting a tiling strategy and loading Q/K/V blocks into on-chip SRAM to avoid exhausting off-chip memory bandwidth due to excessive sequence lengths.
• Pipelining the All-to-All communication of the routing module with operator computation to overlap them in time.

Layer 2: Graph Compiler and Runtime Layer. The chip’s software stack provides graph-compilation-like static optimization capabilities. The adaptation team exports the model’s computation graph from the training framework’s intermediate representation, further applying operator fusion (e.g., QKV fusion, residual and normalization fusion), memory planning, and communication primitive insertion to generate a binary execution plan tailored for the chip. The key benefit of this layer is reducing runtime scheduling overhead and allowing VRAM allocation to be globally managed at compile time.

Layer 3: Inference Serving Layer. For real-world business scenarios, adaptation also covers the integration with the inference serving framework. Specific implementations include request queuing with dynamic batching, model-routing-based request distribution, KV Cache management and reuse, and streaming output capabilities. Notably, because the model supports long-horizon Agent tasks, the inference service must also handle business logic such as multi-turn conversations, tool-calling result concatenation, and timeout control.

4. Key Optimizations for the Inference Path: Beyond “Just Running”

Completing basic adaptation and getting the model to run stably is only the first step. Extracting maximum throughput from limited compute resources represents the true value of a Day-0 adaptation. The following is a review of key optimization points from this practice.

4.1 Communication Compression and Topology-Aware Scheduling

Cross-node communication volume for a 3-trillion-parameter MoE model is massive. In a typical 100-billion activated parameter configuration, each token must communicate with all experts during routing, meaning a single All-to-All communication can reach tens or even hundreds of megabytes. The chip’s adaptation team employed the following strategies:

• Overlapping communication and computation operators. Utilizing an independent communication engine (or DMA channel) to parallelize computation and communication, effectively hiding communication latency.
• Topology-aware routing strategy. Based on inter-node bandwidth differences, frequently collaborating expert groups are placed on the same node or under the same switch to reduce cross-rack traffic.
• Mixed-precision communication. While ensuring convergence accuracy, transmitting certain intermediate activations in BF16 or even FP8 to reduce communication data volume.

4.2 Long-Context Inference: Co-optimizing Memory and Computation

For million-token sequences, the performance characteristics of the traditional prefill phase and decode phase are starkly different. In the prefill phase, computation consists of dense matrix multiplications, making compute power the throughput bottleneck. In the decode phase, generating a single token at a time shifts the bottleneck to memory bandwidth and communication latency. Addressing this, the adaptation team implemented a phased optimization strategy:

• Large-batch chunking in the prefill phase. Super-long sequences are split into multiple chunks, processed in parallel within each chunk, followed by local recomputation for cross-chunk attention, trading memory for compute.
• Hierarchical KV Cache storage in the decode phase. Hot data (KV of recent tokens) is stored in on-chip SRAM or high-bandwidth memory, while cold data (KV of earlier tokens) is placed in standard VRAM. Prefetch instructions are used to move the next required batch of KV into fast memory ahead of time.

4.3 Stability and Availability Verification

The biggest fears in LLM inference are “running out of memory after a while” or “crashing mid-way through a long task.” During adaptation, the team focused on three types of validations:

• Long-term stability testing: Running mixed workloads (short queries, multi-turn conversations, ultra-long document generation) continuously for 7 days to observe VRAM fragmentation, memory leaks, and performance degradation curves.
• Extreme scenario testing: Constructing inputs approaching 1 million tokens to verify the safe boundary management of the KV Cache, while ensuring clear and debuggable error messages when memory is insufficient.
• Fault recovery mechanism: Addressing common single-point failures in multi-node inference by implementing request-level retries and task rescheduling after node removal, ensuring a single node anomaly does not affect overall service availability.

5. Engineering Experiences and Pitfall Guide

Reflecting on the entire adaptation process, the following experiences are worth noting for future projects:

1. Validate communication topology early, not just single-chip compute. Many teams prioritizing domestic chip adaptation look first at single-chip FLOPS or bandwidth metrics. However, actual MoE model throughput is often dictated by communication efficiency. It is recommended to set up a minimal multi-card environment at project inception to empirically test All-to-All bandwidth, latency jitter, and long-distance transmission stability.

2. KV Cache management is a hidden reef for long-context models. If the software stack does not support PagedAttention-like memory management, it is advised to proactively plan a KV Cache memory pool in the computation graph and design a sequence-level memory reuse strategy. Otherwise, a million-token context model might run under small batches, but the probability of OOM spikes once concurrency increases.

3. Graph compilation is not a panacea; runtime scheduling is the fallback. While static graph compilation yields performance benefits, optimizations may fail when facing dynamic shapes (e.g., variable sequence lengths, variable token routing). It is recommended to retain some runtime interpretation capability or insert dynamic branch nodes into the compiled graph to ensure flexibility.

4. Test sets must accurately reflect business distributions. Do not rely solely on fixed synthetic datasets for performance validation. Mix in real user query logs, code completion requests, and multi-turn Agent calls. In practice, we observed that performance under isolated generative task stress tests could differ by 2-3 times compared to mixed workloads.

6. Outlook: The Convergence of Domestic Compute and Top-Tier Models

The Day-0 adaptation of a 3-trillion-parameter model on a domestic AI chip holds significance far beyond merely “getting another model to run.” It validates the following facts:

• The software stack of domestic compute is now capable of engineering adaptations for ultra-large sparse models, moving beyond the “only runs demo examples” stage.
• As model architectures and inference engines rapidly evolve, the deep customization capability of software-hardware co-design is more valuable than sheer hardware peak metrics.
• The prosperity of the open-source model ecosystem provides rich adaptation targets for domestic chips, reversely driving the maturation and refinement of software stacks.

Looking ahead, LLM parameter scales will continue to grow, and architectural innovations like MoE, long context, and multimodal fusion will persist. For domestic AI chips to keep pace in this race, they require not only compute iterations in each hardware generation but also sustained investment in software toolchain usability, open-source development ecosystems, and deep optimization for typical scenarios. The 3-trillion-parameter model adaptation reviewed in this article represents a significant milestone in this long-term engineering journey.

For developers, reading this article should go beyond the news of “a specific chip adapted a specific model.” It should distill a cross-platform methodology for LLM deployment—regardless of the compute hardware used in the future, understanding the model’s communication characteristics, memory distribution, and scheduling granularity will always remain the cornerstone of high-performance inference.