Adapting Trillion-Parameter Models for Inference on Domestic AI Accelerators: Practice and Insights

1. Background: Deployment Challenges of Trillion-Parameter Models

In recent years, large language models have continued to scale from hundreds of billions to trillions of parameters. At the same time, model capabilities have expanded beyond text-only inference to incorporate multimodal inputs such as images, raising the bar for underlying compute platforms, memory management, communication bandwidth, and serving frameworks.

Against this backdrop, a key concern for developers and enterprise users is how to quickly adapt extremely large models to domestic accelerator hardware. Adaptation is not just about “getting the model to run.” More importantly, it requires establishing a complete pipeline from model loading and service startup to basic inference, while validating the software stack’s ability to work cohesively in a heterogeneous environment.

Take a recently open-sourced three-trillion-parameter model as an example. Its size far exceeds the memory capacity of a single accelerator, making multi-device coordination and memory sharding strategies essential. The model also supports a context length of one million tokens, meaning that inference must maintain extremely long key-value caches. This places higher demands on memory allocators and memory management mechanisms. If the underlying software stack cannot manage these resources efficiently, the serving process may fail at startup due to out-of-memory errors or communication timeouts.

2. Adaptation Goals and Technical Approach

The core objectives of this adaptation effort can be summarized as follows: first, achieve Day-0 model loading on domestic accelerators; second, successfully start the inference service; and third, validate both text and image inference pipelines. Day-0 support means that once the model is officially open-sourced, developers can immediately begin experimenting and deploying on domestic hardware environments rather than waiting for subsequent patches or third-party ports.

To achieve these goals, the technical approach centers on a unified hardware-software stack built around accelerators designed for both training and inference. A unified training-inference design means the same hardware can handle high-throughput training workloads and switch to low-latency inference serving. This places significant demands on both hardware architecture and software scheduling. The unified software stack, in turn, abstracts away hardware-specific details and provides standardized compute, communication, and memory management interfaces to upper-level frameworks.

In practice, the adaptation process is typically divided into three phases:

1. Model architecture parsing and weight mapping: converting model weights from a standard format into a sharded format suitable for the local acceleration environment.
2. Memory planning and multi-device coordination: designing tensor parallelism, pipeline parallelism, or hybrid parallelism strategies based on the model’s layer count and attention head count.
3. Serving encapsulation and inference validation: wrapping the model into a callable inference service and validating outputs through standard inputs.

3. Model Loading: From Weight Formats to Memory Planning

Loading an extremely large model is not simply a matter of reading files into memory. Since the weight files can reach hundreds of gigabytes or more, no single accelerator can hold the entire model. Sharded loading and inter-layer offloading strategies are therefore required.

A common approach is to partition the model by layers during the loading phase, with each accelerator responsible for hosting the weights of a subset of layers. When an inference request arrives, the data flows through the model shards on each device, with intermediate activations passed over a high-speed interconnect. This significantly reduces per-device memory requirements, but it also demands efficient cross-device communication primitives and synchronization mechanisms from the software stack.

Memory planning must account for three categories of resource usage:

• Model weights: the static portion of memory that remains largely unchanged after loading.
• Key-value caches: memory that grows dynamically with context length and is the primary consumer of memory in ultra-long-context scenarios.
• Activations: temporary tensors created during inference that have short lifespans but are frequently allocated and released.

Sound memory planning should establish clear boundaries among these three categories and define policies for dynamic expansion. For example, when user requests have shorter context lengths, more communication buffers can be allocated to improve throughput. When context lengths approach the model’s upper limit, priority should shift to ensuring contiguous key-value cache allocation to avoid fragmentation-induced failures.

4. Service Startup: Engineering Details for a Working Inference Pipeline

Service startup is the critical link between the model and incoming requests. A typical inference service startup flow includes: initializing compute devices, loading model shards, establishing communication groups, warming up the inference engine, registering request handling interfaces, and starting health checks.

In practice, issues tend to cluster in three areas:

First, distributed initialization timeouts. When multiple devices collaborate on inference, all participating devices must complete communication group setup and handshaking. If one node’s model shards load too slowly, the entire initialization process may time out. A layered initialization strategy can address this: first complete local model loading and single-device compute warmup, then perform cross-device communication handshakes and global consistency checks.

Second, memory fragmentation causing service startup failures. Extremely large models repeatedly allocate and release large memory blocks, which can create fragmentation and eventually prevent subsequent allocation requests from being satisfied. To mitigate this, a memory defragmentation pass can be performed before service startup to consolidate contiguous free space, or a unified memory pool management strategy can be adopted to reduce the uncertainty of dynamic allocation.

Third, high first-request latency. When the inference engine receives its first request, it often needs to perform one-time preparation work such as operator compilation, cache allocation, and execution graph optimization, leading to significantly longer response times. A warmup mechanism is the standard solution: send several dummy requests before the service officially goes live to trigger all initialization paths.

5. Multimodal Inference: Unified Handling of Text and Image Pipelines

Since the model supports both text and image inputs, the adaptation effort must cover not only pure language scenarios but also mixed text-image inference pipelines. Multimodal models typically consist of a vision encoder and a language model backbone, which differ significantly in computational characteristics, tensor shapes, and parallelism strategies.

The inference flow typically follows a two-stage “encode first, then generate” pattern. Image inputs are first converted into feature vector sequences by the vision encoder, then concatenated with text tokens and fed into the language model backbone for autoregressive generation. This flow requires the software stack to handle the possibility that the vision encoder and the language backbone may run under different parallelism configurations, necessitating tensor rearrangement or inter-device data movement when switching between stages.

Validation typically starts with simple cases, such as a single image paired with short text, and gradually progresses to mixed requests with multiple images and long text. Each case must cover the end-to-end pipeline—from frontend input parsing and tensor preprocessing to model forward computation and output decoding—to ensure that all stages run stably under the unified software stack.

6. Full-Stack Software Coordination: The Key to Adaptation Efficiency

Looking back at the entire adaptation process, the speed at which Day-0 support can be achieved depends critically on the depth of software stack coordination. “Full stack” here spans device drivers, runtime libraries, compilers, communication libraries, and upper-level inference frameworks.

At the driver and runtime level, stable memory management interfaces and compute stream scheduling capabilities are needed to overlap data movement across devices with compute tasks, reducing idle waiting. At the compiler level, the large number of matrix multiplications and attention computations in extremely large models requires generating efficient operators tailored to local hardware characteristics while avoiding uncontrollable compilation times from excessive optimization. At the communication library level, primitives for tensor parallelism and pipeline parallelism must be provided, along with adaptive routing support across different topologies.

For developers, good software stack coordination means being able to focus on model logic and business adaptation rather than low-level hardware details. This is also an important criterion for evaluating the maturity of a heterogeneous acceleration platform: whether it can encapsulate underlying complexity into predictable, debuggable, and reusable abstractions.

7. Summary and Future Optimization Directions

This adaptation effort validated the feasibility of running extremely large model inference on domestic full-featured accelerators. Model loading, service startup, and basic text and image inference pipelines were all successfully established, laying the groundwork for deeper performance tuning and production-grade deployment.

Future optimization directions include:

1. Inference performance tuning: optimizing request scheduling and batching strategies for high-concurrency scenarios to improve throughput and resource utilization.
2. Ultra-long-context optimization: exploring more efficient key-value cache compression and retrieval mechanisms to reduce memory pressure in ultra-long-context scenarios.
3. Deepening multimodal pipelines: refining the preprocessing pipeline for mixed text-image requests to reduce data movement and format conversion overhead.
4. Deployment standardization: codifying the lessons learned into reusable deployment templates and automation scripts to lower the barrier for adapting future models.

For enterprises and developers, establishing a methodology of “rapid validation—incremental optimization—standardized deployment” offers more long-term value than chasing extreme performance in a single adaptation effort. Given the rapid iteration of domestic accelerator hardware and software ecosystems, this practical approach will help teams respond more confidently to technological changes and identify the best combination of models and compute platforms for their real-world workloads.