Scaling ML Models Efficiently With Shared Neural Networks

As machine learning models grow in complexity and size, organizations face increasing challenges in deploying and scaling these models efficiently. A particularly pressing challenge is balancing hardware memory constraints with the expanding size of ML models while maintaining high performance and cost-effectiveness. This article explores an innovative architectural solution that addresses these challenges through a hybrid approach combining shared neural encoders with specialized prediction heads.

The Challenge: Memory Constraints in ML Model Deployment

Traditional machine learning deployments often require loading complete models into memory for each distinct use case or customer application. For example, in natural language understanding (NLU) applications using BERT-based models, each model typically consumes around 210-450 MB of memory. When serving thousands of customers, this leads to significant scaling challenges. A typical server with 72 GB of CPU memory can only support approximately 100 models simultaneously, creating a hard ceiling on service capacity.

A Novel Solution: Decoupled Architecture

To address these scaling challenges, we can implement a decoupled architecture that separates models into two key components. The first is the Shared Neural Encoder (SNE), a pre-trained neural network component that handles the fundamental encoding of input data. In practice, this encoder, based on BERT architecture, generates contextual embeddings - 768-dimensional vectors for each token in the input text. The second component is the Task-Specific Prediction Head (TSPH), a smaller specialized component that processes these embeddings for specific predictions. This architecture allows multiple customer applications to share the same encoder while maintaining their unique prediction capabilities through individual specialized components.

Key Components and Their Interactions

Shared Neural Encoder

The Shared Neural Encoder leverages pre-trained models extensively trained on large generic datasets. In our implementation, the encoder component typically requires about 227 MB in its non-quantized form, but through INT8 quantization, this can be reduced to approximately 58 MB. When processing text, it handles sequences of up to 250 tokens, producing high-dimensional contextual embeddings. For a typical utterance of 15 tokens, the encoder generates output tensors of size 15x768, requiring about 45 KB of memory for the embeddings.

Task-Specific Prediction Head

The Task-Specific Prediction Head represents a dramatic improvement in efficiency, requiring only 36 MB in its non-quantized form and as little as 10 MB when quantized. This component maintains custom configurations for tasks like intent classification and entity recognition while consuming significantly less memory than traditional full models. For example, a single prediction head can process the 15x768 dimensional embeddings and output classification scores in under 5 milliseconds on standard CPU hardware.

Performance Metrics and Implementation

Our testing with this architecture has revealed impressive performance characteristics. Using a c5.9xlarge instance with 36 vCPUs and 72 GB of memory, a single server can handle approximately 1,500 transactions per second (TPS) when running prediction heads alone, with a p90 latency of 25ms. When combining both components on the same hardware, we still achieve 300 TPS with a p90 latency of 35ms - more than adequate for most production workloads.

The encoder component, when deployed on GPU hardware (specifically a p3.2xlarge instance), can process over 1,000 requests per second with a p90 latency of just 10ms. This configuration allows for efficient batching of requests, with optimal batch sizes of 16 requests providing the best balance of throughput and latency.

Infrastructure Design

The implementation utilizes a cellular architecture approach with specific performance targets. Each cell in the architecture is designed to handle approximately 1,000 active models, with auto-scaling capabilities that maintain cell health across three availability zones. The system employs a sophisticated load-balancing strategy that can handle bursts of up to 600 TPS per cell while maintaining p99 latencies under 80ms.

For high availability, the architecture implements a multi-ring design where each ring serves specific language locales. For example, all English language variants might share one ring while French variants share another, allowing for efficient resource allocation based on language-specific model characteristics. Each ring contains multiple cells that can scale independently based on demand.

Resource Optimization

The decoupled architecture achieves significant resource optimization. In a production environment, actual measurements show:

• Memory reduction. The original 210 MB models are reduced to 10 MB prediction heads plus a shared 58 MB encoder
• Storage efficiency. A single encoder can serve thousands of prediction heads, reducing total storage requirements by up to 75%
• Cost efficiency. GPU resources are shared across all requests to the encoder, while cheaper CPU resources handle prediction head processing
• Latency improvement. End-to-end processing time reduced from 400ms to approximately 35ms for cached models

Future Implications

This architectural pattern opens new possibilities for ML deployment, particularly as model sizes continue to grow. With current trends pointing toward larger language models, the ability to share encoded representations becomes increasingly valuable. The architecture supports future growth through:

• Dynamic model updates. New encoder versions can be deployed without requiring updates to prediction heads
• Flexible scaling. Independent scaling of encoder and prediction components based on specific workload characteristics
• Resource pooling. Efficient sharing of GPU resources across thousands of customers

Conclusion

The decoupled architecture combining shared neural encoders with task-specific prediction heads represents a significant advancement in ML model deployment. With concrete performance improvements such as 75% memory reduction, 40% latency improvement, and support for thousands of concurrent models, this approach provides a practical solution to scaling challenges while maintaining performance and controlling costs.

For organizations looking to implement similar architectures, careful consideration should be given to specific workload characteristics. The success of such implementations depends on thoughtful infrastructure design and intelligent resource management strategies supported by robust monitoring and scaling policies.