Deep learning models achieve high accuracy in tasks like speech recognition and image classification, often surpassing human performance. 1 However, they require large training datasets and significant computational power. Cloud inference provides a scalable solution to handle these demands efficiently.
Explore cloud inference, compare it to on-device inference, and highlight its benefits and challenges. Here are some leading tools that can be deployed in cloud inference:
What is inference?
Figure 2. Visualization of how inference works.
Inference is the phase where a trained model processes new data to make predictions or decisions. The model applies the learned weights without further adjustment.
For example, if a model is trained on cat images, it should be able to identify whether a new image contains a cat. However, this process involves billions of parameters and requires high computational resources. Cloud inference addresses this challenge.2
What is cloud inference/inference as a service?
To keep up with the growing demand for machine learning (ML) inference, Meta has increased its infrastructure capacity by 250%.3 This reflects a broader trend of organizations seeking scalable, high-performance solutions to efficiently handle inference workloads.
Cloud inference refers to running machine learning models on cloud platforms instead of local hardware. This approach allows users to access powerful computational resources remotely, leveraging cloud-based GPUs, TPUs, and other specialized hardware. Since these components are expensive and complex to maintain on-premises, cloud inference offers a practical alternative for businesses that need scalable AI capabilities without heavy infrastructure investments.
Cloud inference for large language models (LLMs)
With the rise of generative AI, cloud inference is increasingly driven by large language models (LLMs) such as GPT-style and open-weight models. These models introduce new requirements compared to traditional ML inference:
- High memory usage: LLMs require significant GPU memory, especially for long context windows.
- Token-based processing: Costs and latency depend on input/output tokens rather than batch size.
- Throughput optimization: Techniques like batching and KV-cache reuse are critical for efficiency.
To address these challenges, cloud providers and open-source tools now focus on LLM-specific inference optimizations, such as:
- Continuous batching
- Speculative decoding
- Quantization (e.g., 4-bit, 8-bit models)
Cloud inference vs. inference at source
The primary distinction between cloud inference and on-device (or edge) inference lies in where the computation takes place:
- On-device inference (edge inference) processes data directly on the device where it’s generated, such as an IoT sensor or smartphone.
- Cloud inference sends data to remote cloud servers for processing before returning the results.
Each approach has its advantages. On-device inference offers lower latency and greater privacy since data remains local, but it is limited by the device’s hardware. Cloud inference, on the other hand, provides access to high-performance computing power but relies on internet connectivity and can introduce latency.
In practice, many organizations adopt a hybrid inference approach, where latency-sensitive tasks are handled on-device while more complex processing is offloaded to the cloud. This balances performance, cost, and data privacy requirements.
Cloud inference in inference time computing
Inference-time computing refers to the computational workload required to generate predictions from trained AI models. As models grow in size and complexity, this stage has become a key performance and cost driver in AI systems.
Cloud inference supports this process by:
- Scaling resources dynamically: Cloud platforms allocate compute based on demand, avoiding over-provisioning.
- Accelerating processing: Specialized hardware (GPUs, TPUs, FPGAs) reduces inference latency.
Shift toward inference-time optimization
The growing cost of running large-scale AI models has shifted industry focus from training efficiency to inference-time optimization. This shift is driven by open-weight models and more compute-intensive architectures, making inference a primary bottleneck in production environments.
Infrastructure and optimization techniques
Cloud providers are addressing these challenges by introducing specialized infrastructure and optimization methods:
- Custom AI chips: AWS Inferentia, Google TPU v5e, and Azure Maia improve price-performance for inference workloads.
- Model optimization: Quantization and pruning reduce compute requirements with limited impact on accuracy.
- Efficient serving engines: Systems like vLLM and TensorRT-LLM increase throughput via optimized memory usage and batching strategies.
Advantages of cloud inference
1- It is time sensitive
Cloud-based solutions can deliver low latency when deployed close to users via regional infrastructure, but may introduce delays compared to on-device inference in latency-critical applications.
2- It works anywhere
Cloud-based solutions are inherently global, offering the advantage of location independence and the ability to operate across different geographical regions.
3- It has a strong battery life
By offloading heavy computational tasks to cloud servers, there is a significant reduction in the power consumption of local devices.
Key challenges of cloud inference
1- Data leaks
Consider the operation of recommendation systems; they are developed through training on substantial volumes of data, much of which is personal in nature. When inference is conducted utilizing this trained dataset and the process is executed in a cloud environment, there arises a potential risk that the data could be accessed by unauthorized parties.
2- Attacks
Models used in cloud inference are susceptible to various forms of cyberattacks. For instance, most current cloud inference attacks are targeted at images.5 These attacks can compromise the integrity and confidentiality of sensitive data.
Strategies to prevent attacks
To safeguard against attacks in cloud-based inference systems, the following strategies can be implemented:
- Cryptographic methods: They are used to prevent unauthorized replication of training data and access to the model. An example would be encrypting the cloud-based models. Encryption acts as a barrier, ensuring that even if an attacker gains access to the model, they cannot easily understand or replicate the training data.
- Controlled noise: You can minimize the risk of information leakage from the model’s output by adding designed noise to the output vector of the model. This will make it more difficult for attackers to extract meaningful data.
- Prevent overfitting: Overfitting makes a model too tailored to the training data, potentially revealing sensitive information. By randomly removing some connections, or edges, during training, the model becomes less prone to overfitting.
3- Costs
Cloud inference requires balancing accuracy, latency, and cost efficiency. Running large models with billions of parameters can be expensive, particularly when handling high-resolution data or scaling inference workloads.
Many cloud inference services now charge based on the number of input and output tokens processed, making cost estimation dependent on usage patterns rather than infrastructure alone.
4- Network delays
While cloud inference aims for low-latency performance, network delays and bandwidth limitations can impact real-time applications, especially when transmitting large datasets.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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