Splitting the Inference Load: Privacy and Speed for Large Language Models

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Author: Denis Avetisyan

A new system efficiently distributes large language model computations between local devices and the cloud to address both privacy concerns and network latency.

This work demonstrates a practical approach to privacy-aware split inference with speculative decoding, mitigating inversion attacks and achieving viable interactive performance over wide-area networks.

Deploying large language models is often hampered by privacy concerns and network latency when utilizing cloud resources. This paper, ‘Privacy-Aware Split Inference with Speculative Decoding for Large Language Models over Wide-Area Networks’, presents a system that addresses these challenges by splitting model computation between a local device and the cloud, while employing lookahead decoding to mitigate high-latency communication. Our results demonstrate viable performance-achieving 7.8-9.3 tokens/second-and a tunable privacy-performance tradeoff, alongside formal verification of decoding accuracy. Will this approach unlock truly interactive LLM experiences across geographically distributed networks and resource-constrained devices?

The Inevitable Bottleneck: LLM Latency and Why Speed Always Lags

Large Language Models, underpinned by the powerful Transformer architecture, have demonstrated remarkable capabilities in natural language processing, achieving state-of-the-art results across a spectrum of tasks. However, translating this performance into practical applications is often hindered by substantial latency during the inference process – the time it takes for the model to generate a response. While model accuracy continues to improve, the computational demands of these increasingly complex networks create a significant bottleneck. Each prediction requires numerous sequential calculations, and the sheer size of these models-often containing billions of parameters-necessitates considerable data transfer and processing, leading to delays that can be unacceptable for real-time interactions or time-sensitive applications. This trade-off between performance and speed remains a central challenge in deploying LLMs effectively.

The substantial latency experienced with large language models isn’t simply a matter of computational power, but a consequence of how these models generate text and their sheer scale. Decoding, the process of converting probabilities into coherent output, happens sequentially – each new word is predicted based on all preceding words, creating a dependency chain. This inherent serial nature limits parallelization. Compounding this is the massive communication overhead; these models, often comprising billions of parameters, frequently reside on distributed systems. Every prediction necessitates transferring data between processing units and memory, a process significantly slowed by network bandwidth and distance. Therefore, even with powerful hardware, the time required to move data can become the dominant factor limiting response speed, especially as model sizes continue to grow.

The speed at which Large Language Models (LLMs) can respond is frequently limited not by the models themselves, but by the time it takes for data to travel to and from the cloud servers hosting them – a metric known as Network Round Trip Time (RTT). This delay, though often measured in milliseconds, accumulates significantly during the iterative process of generating text, where each new token requires a request to the server and a response. Consequently, applications demanding immediate interaction, such as real-time translation, conversational AI, or live captioning, experience noticeable lag. Minimizing RTT is therefore critical; strategies include model parallelism, quantization, and increasingly, edge deployment – bringing the LLM closer to the user to reduce the physical distance data must traverse and enhance responsiveness.

The Pragmatic Approach: Distributing the Load with Split Inference

Split inference addresses latency challenges in Large Language Model (LLM) deployments by distributing the computational load across both edge devices and cloud infrastructure. This paradigm shifts away from solely cloud-based processing, enabling a portion of the LLM’s operations to occur locally on the edge device. By strategically partitioning the model’s workload, split inference aims to reduce reliance on continuous communication with the cloud, thereby minimizing network latency and improving overall response times. This distributed approach is particularly relevant in scenarios with limited or unstable network connectivity, and allows for faster initial processing of user inputs directly on the edge.

Layer splitting is the core mechanism enabling split inference; it involves partitioning the layers of a large language model and distributing their execution between edge devices and cloud servers. Each layer performs a specific transformation on the input data, and the assignment of these layers is not arbitrary. Typically, earlier layers, responsible for initial token processing and feature extraction, are offloaded to the edge device for immediate processing. Subsequent layers, requiring more substantial computational resources or access to broader knowledge bases, remain in the cloud. This strategic assignment minimizes data transfer requirements and reduces latency by allowing the edge device to process initial tokens locally before transmitting intermediate results to the cloud for completion.

Offloading the initial layers of a Large Language Model (LLM) to edge devices reduces reliance on consistent, high-bandwidth cloud connectivity, directly impacting latency. This approach minimizes the data transferred to and from the cloud for each inference request, as early layer computations are performed locally. Testing with a Mistral 7B model over a simulated wide area network (WAN) with approximately 80ms latency demonstrated a throughput of 8-9 tokens per second, indicating a substantial performance improvement compared to fully cloud-based inference under similar network conditions.

Beyond Greed: Optimizing Decoding with Speculative Parallelism

Traditional greedy decoding operates by requesting a single token from the language model at each step and subsequently submitting the generated token back to the cloud for the next inference request. This iterative, step-by-step communication pattern introduces significant latency, as each token generation is dependent on the completion of a full round-trip time (RTT) between the client and the cloud service. The inherent sequential nature of this process prevents any parallelization of token generation, directly correlating the total decoding time with the number of tokens produced and the network latency between the client and the language model server.

Lookahead Decoding mitigates the latency issues of traditional Greedy Decoding by performing speculative parallel token generation. Instead of requesting each token sequentially from the cloud, this technique proactively generates multiple tokens locally before a round trip is completed. This reduces the total number of required communication cycles between the client and the cloud service. The system speculatively generates tokens, and while not all speculative steps are ultimately accepted, the overall reduction in round trips provides a measurable performance improvement, particularly for larger models and longer sequences.

The Acceptance Rate is a key performance indicator for Lookahead Decoding, quantifying the proportion of speculatively generated tokens that are ultimately accepted as correct by the model. Empirical results demonstrate consistent performance across model sizes, with the Acceptance Rate averaging between 1.21 and 1.25 tokens generated per decoding step for both the 7 billion and 12 billion parameter models. This indicates that, on average, each speculative step successfully produces more than one valid token before requiring verification, contributing to the overall reduction in round trip times and improved decoding latency.

To accurately measure the latency reduction achieved through Lookahead Decoding, an RTT (Round Trip Time) Decomposition Model was implemented. This model breaks down the overall latency into constituent components, allowing for precise quantification of the benefits derived from speculative decoding. Validation of the model, performed using cross-validation techniques, demonstrates a consistent error rate of less than 6.2% when applied to both the 7B and 12B parameter models. This low error rate confirms the model’s reliability in assessing the performance gains of the Lookahead approach across different model sizes.

The Illusion of Privacy: Mitigating Risks in Distributed Inference

Split inference represents a paradigm shift in machine learning deployment, prioritizing data privacy by strategically partitioning computational tasks. Rather than transmitting raw data to a centralized server for complete processing, split inference distributes the workload, keeping sensitive computations directly on the user’s device. This is particularly effective when coupled with the implementation of ‘local layers’ – portions of the neural network that reside and execute entirely on-device. By performing initial processing locally, such as feature extraction or embedding generation, the exposure of sensitive input data during transmission is significantly reduced. This approach doesn’t eliminate data transfer entirely, but minimizes the amount of private information that leaves the device, offering a robust and practical method for enhancing user privacy in increasingly data-driven applications.

The initial step in many machine learning models, the embedding layer transforms input tokens-like words or parts of words-into numerical representations. Executing this layer locally, on the user’s device, provides a significant privacy advantage. Because the embedding process is where raw input begins to be encoded, keeping it off external servers prevents sensitive information from being immediately exposed during transmission. This localized computation reduces the attack surface for potential adversaries, as the core of the input’s semantic meaning never leaves the device. Furthermore, it minimizes the amount of potentially identifying data that needs to be protected in transit, bolstering overall privacy even before other security measures, such as secure connections, are implemented. By confining this critical initial processing to the user’s environment, the risk of data breaches and privacy violations is substantially diminished.

Protecting data as it moves between components of a split inference system requires robust security measures, and establishing secure connections is paramount. Techniques like SSH Tunneling create encrypted pathways for data transmission, shielding sensitive information from potential eavesdropping or tampering during transit. This is particularly vital when offloading computations to remote servers, as standard network connections can be vulnerable. By encapsulating data within an encrypted tunnel, SSH effectively establishes a private, secure communication channel, ensuring that even if intercepted, the information remains unintelligible to unauthorized parties. Implementing such safeguards isn’t merely about preventing data breaches; it’s about building user trust and maintaining the integrity of the entire inference pipeline.

Despite the privacy advantages of split inference and local computation, systems remain vulnerable to inversion attacks – scenarios where an adversary attempts to reconstruct the original input data from the model’s outputs. Recent investigations reveal that with only two locally processed layers, such attacks achieve an accuracy of approximately 59%. However, a notable improvement in security emerges as the number of local layers increases; the observed accuracy of inversion attacks decreases to around 35% when employing eight locally processed layers. This demonstrates that increasing the extent of on-device computation effectively raises the bar for potential adversaries, bolstering the overall privacy protections inherent in split inference architectures.

The pursuit of efficient large language model inference, as detailed in this work, inevitably courts future complications. This system, dividing computation and attempting to mask latency with speculative decoding, feels less like a solution and more like a deferral of inevitable tech debt. One recalls Ken Thompson’s observation: “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” The elegance of splitting inference and predicting network behavior is seductive, yet production environments will undoubtedly expose unforeseen vulnerabilities and performance bottlenecks. If a bug is reproducible, this system, at least, provides a stable foundation upon which to build – and then inevitably refactor.

What’s Next?

This work, predictably, doesn’t solve privacy or latency. It merely relocates the problems. One imagines production environments will rapidly reveal that ‘viable performance’ is a moving target, especially when confronted with adversarial inputs designed to maximize cloud-side computation – or, more likely, when someone inevitably tries to scale this beyond a single moderately-sized model. The claim of mitigating inversion attacks feels… optimistic. The history of machine learning security suggests every solved problem merely begets a more subtle vulnerability.

Future research will, of course, focus on increasingly elaborate speculative decoding schemes. The pursuit of ‘lookahead’ will become a race against diminishing returns, attempting to hide latency that is fundamentally limited by the speed of light and the cost of communication. One suspects the real innovation won’t be in the algorithms, but in increasingly sophisticated network monitoring tools designed to detect – and quietly work around – inevitable failures.

Ultimately, this feels less like a breakthrough and more like a temporary reprieve. It addresses symptoms, not causes. Everything new is just the old thing with worse docs, and this system will, in time, be another layer of complexity to debug when the next ‘revolutionary’ model arrives.

Original article: https://arxiv.org/pdf/2602.16760.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-21 19:41