Adapting Large-Scale Parameter Models on Domestic Accelerator Cards: Inference Strategies and Practices

1. Background: Deployment Challenges of Ultra-Large Parameter Models

Large language models have been advancing rapidly in code generation, complex reasoning, and long-horizon task planning. When parameter counts reach the trillion level, the challenge for inference systems shifts from “how fast can it compute” to “can it run reliably at all.”

Consider a dense model at the 3-trillion-parameter scale. Using standard FP16 precision, the weights alone require roughly 6 TB of GPU memory. Even with 8-bit quantization, the weight footprint remains around 3 TB. A single accelerator card cannot handle this, so multi-card collaboration and hierarchical memory management become essential. Long-context support further amplifies KV cache pressure. For a model supporting 1 million tokens of context, KV cache under concurrent inference can exceed the weight size itself, becoming the dominant memory consumer.

Adapting ultra-large models therefore requires addressing three core questions: how to partition the model, how to load the weights, and how to optimize the inference path.

2. Model Architecture and Computational Characteristics

Adaptation typically begins with decomposing the model architecture. Most mainstream large models use a decoder-only structure comprising embedding layers, multiple self-attention blocks, feed-forward networks, and an output layer. At the trillion-parameter scale, a single layer may contain billions or even tens of billions of parameters, with the layer’s weights alone exceeding a single card’s memory.

Take the common multi-head attention mechanism as an example. Each layer contains query, key, value, and output projection matrices, plus the up-projection and down-projection matrices in the feed-forward network. In long-sequence scenarios, attention computation grows quadratically with sequence length, while feed-forward computation grows linearly. However, the former’s memory footprint is more heavily affected by KV cache.

During adaptation, it is important to examine the layered structure and identify each layer’s parameter size, activation size, and communication requirements. Different layers exhibit distinct computational and communication profiles: attention layers are more dependent on matrix multiplication and memory bandwidth, while feed-forward layers demand higher compute density. These differences inform the design of parallelization strategies.

3. Memory Planning and Sharding Strategies

When model size far exceeds a single card’s capacity, memory planning must come before compute optimization. Common approaches include tensor parallelism, pipeline parallelism, and expert parallelism. For dense models, tensor parallelism and pipeline parallelism are the primary choices.

Tensor parallelism splits the weight matrices within a single layer—by column or by row—across multiple cards. Each card holds a shard of the parameters and performs local computation, then communicates to aggregate results. For example, a feed-forward up-projection matrix of shape [hidden, intermediate] can be split by columns so that each card handles a subset of output channels. This reduces per-card weight storage and distributes activation pressure.

Pipeline parallelism partitions the model across layers, assigning groups of layers to different devices. During inference, data flows sequentially through the stages, forming a pipeline. This approach has lower communication frequency but can lead to device idle time, so it is often combined with micro-batching to improve utilization.

The tensor parallelism sharding logic can be illustrated as follows:

# Pseudocode: sharding a feed-forward up-projection matrix by column across cards
weight = load_weight("ffn.up_proj")          # [hidden, intermediate]
shard_size = weight.shape[1] // world_size
local_weight = weight[:, rank * shard_size:(rank + 1) * shard_size]
local_output = activation @ local_weight
global_output = all_gather(local_output)

In practice, tensor and pipeline parallelism are frequently combined. For instance, high-speed interconnects within a single node can be used for tensor parallelism, while pipeline parallelism handles inter-node distribution, balancing communication overhead against memory usage.

4. Inference Pipeline Migration and Operator Adaptation

After model loading and sharding, the inference pipeline must be migrated to the target compute platform. This “migration” is not a simple code copy—it requires adaptation to the underlying programming model and compute libraries.

The general approach is to export the model to an intermediate representation, then map operators based on the target platform’s development stack. For ultra-large models, the focus is on core operators: matrix multiplication, attention computation, normalization, activation functions, and sampling. Matrix multiplication typically dominates inference latency and must align closely with the hardware’s compute units. Attention computation is expensive under long sequences and may need to be replaced with lower-complexity implementations. Normalization and activation functions, while individually lightweight, are called frequently and can affect overall efficiency.

Verification usually proceeds in two stages: first, single-card validation to confirm numerical correctness and operator compatibility; then, multi-card validation to check communication logic, distributed initialization, and long-running stability.

5. Long-Context and KV Cache Optimization

KV cache at the million-token scale is a critical concern in ultra-large model inference. Suppose a model has 128 layers, 8 key-value heads per layer, and 128 dimensions per head—KV cache for a 1-million-token sequence can reach hundreds of gigabytes or even terabytes.

To manage KV cache size, the industry widely adopts grouped-query attention (GQA) or cache compression. GQA reduces the number of key-value heads by having multiple query heads share the same key-value group, significantly cutting cache usage with minimal impact on quality. Cache compression merges, prunes, or quantizes key-value pairs for historical tokens to further reduce storage.

In deployment, cache should be allocated dynamically based on request length rather than reserving large fixed blocks for short requests. Pool-based cache management improves memory utilization and supports higher concurrency.

6. Stable Operation and Engineering Safeguards

For enterprise users, stable operation often matters more than peak performance. Adaptation work therefore needs to cover exception handling, resource monitoring, and recovery mechanisms.

In distributed inference, a single-card failure can disrupt the entire task. Engineering practice typically involves heartbeat detection and checkpoint recovery so that when a device fails, the task can be quickly diagnosed and restarted. Inference services should also support dynamic scaling, adjusting compute resources based on request volume.

On the monitoring side, key metrics include memory usage, compute utilization, communication bandwidth, and request latency. Continuous collection and analysis of these metrics helps identify bottlenecks during long-running operation and provides data for further optimization.

7. Conclusion

Adapting trillion-parameter models is a systematic effort spanning model analysis, memory planning, parallelism strategies, operator optimization, and engineering safeguards. The core objective is not to chase a single performance metric but to make the model runnable, scalable, and maintainable within resource constraints.

For developers, mastering general sharding approaches and migration methods is more valuable than relying on any single hardware or software stack. As model parameter counts continue to grow, the ability to design and optimize inference systems will become a key competitive advantage for technical teams.