Performance benchmarking of on-device versus cloud large language model (LLM) inference times helps developers, product teams, and researchers understand the trade-offs in latency, privacy, cost, and user experience between these two approaches. This guide will provide a clear, step-by-step methodology to measure and compare inference times of LLMs running locally on devices versus in the cloud, highlighting practical considerations to make informed deployment decisions.

What You Will Learn #

This guide explains how to set up benchmarking experiments for on-device and cloud LLM inference, collect and interpret timing data, and evaluate the impact of hardware, network, and model characteristics. You will also find tips to avoid common pitfalls and best practices for meaningful results.

Prerequisites #

• Access to or installation of at least one large language model capable of running both on-device and in the cloud. On-device models are typically smaller, quantized versions optimized for edge hardware.
• A local device (e.g., smartphone, laptop, or edge server) capable of running the on-device LLM.
• Access to a cloud-based LLM API or infrastructure.
• Basic programming skills to automate requests and capture timing (e.g., Python scripts).
• Tools to measure local compute times and network latency.

Step 1: Define Benchmarking Goals and Metrics #

Clarify what aspects of performance you want to compare:

1. Latency: Measure inference time from query input to response output.
2. Throughput: Number of tokens generated per second.
3. Time-to-First-Token (TTFT): How quickly the model starts producing output.
4. Consistency: Variability in inference time (important for user experience).
5. Energy Usage (On-Device): Optional, if tools are available to capture power consumption.

Focus primarily on inference latency as the key metric to capture user-perceived responsiveness.

Step 2: Prepare Your Test Environment #

For On-Device LLM: #

1. Deploy or download an optimized LLM model for your device.
2. Make sure appropriate AI frameworks and runtimes are installed (e.g., TensorFlow Lite, Core ML, or other edge AI runtimes).
3. Prepare the device’s conditions to be as consistent as possible (disable unrelated apps, charge battery sufficiently, and avoid thermal throttling).
4. Use profiling tools to capture precise inference timing on-device, including token generation speed (TGS) and TTFT, if possible.

For Cloud LLM: #

1. Obtain API access to a cloud LLM provider.
2. Ensure a stable and representative internet connection (note network latency considerations).
3. Prepare scripts to send queries and record round-trip inference times including network delays.
4. If possible, test from different geographic locations or simulate network conditions to observe effects.

Step 3: Design Your Test Queries #

• Prepare a set of standardized input prompts varying in length and complexity.
• Include concise prompts to test response latency and longer prompts to assess throughput and stability.
• Use identical prompts for both on-device and cloud tests to ensure comparability.

Step 4: Implement Timing Measurement #

On-Device Timing: #

• Measure time starting immediately before input passes to the model.
• Stop timing when the model finishes generating the output tokens.
• Ideally, break down timing into “time to first token” and total generation time.
• Repeat multiple runs to smooth variability.

Cloud Timing: #

• Measure end-to-end round trip time including transmission over the network.
• Separate network latency from server inference time if possible (some cloud providers return inference time metadata).
• Repeat tests multiple times to average timings.

Step 5: Collect and Analyze Results #

1. Latency Comparison: Calculate average and median inference times. On-device inference typically offers more consistent, often sub-second latency, while cloud inference depends heavily on network conditions with typical latencies often between 1.4 to 1.8 seconds or more per request[3].
2. Throughput: On-device token generation speed can approach human reading speed benchmarks on powerful edge devices but may be limited by memory and thermal constraints[5][7].
3. Variability: Cloud results may be less consistent due to network congestion and server load.
4. Impact of Model Size: Larger models likely run only in cloud environments; on-device models are often smaller due to hardware constraints but provide faster, localized results[1][2][7].
5. Privacy Implications: On-device inference keeps user data local, preserving privacy, whereas cloud inference involves data transmission and storage, raising potential concerns[1][2].

Step 6: Document Contextual Factors and Limitations #

When interpreting your benchmarking data, keep in mind:

• Network Variability: Cloud inference times can fluctuate hugely during real-world scenarios.
• Device Capability: CPU, GPU, NPU specs, thermal throttling, and available RAM directly influence on-device results.
• Model Optimization: Quantization, pruning, and distillation improve on-device speed but may degrade accuracy.
• Hybrid Solutions: Pairs small on-device models with cloud fallback to balance latency and capability.

Tips and Best Practices #

• Run tests in controlled environments: To reduce noise, keep network conditions stable during cloud benchmark.
• Run multiple iterations: Average times over several runs to avoid outliers.
• Monitor device health: On-device tests can be affected by overheating or background processes.
• Record environmental context: CPU load, battery level, network speed, and temperature.
• Use consistent software versions: Variations in model or runtime versions impact comparability.
• Consider model accuracy trade-offs: Faster models are often smaller or quantized and may perform differently.

Common Pitfalls to Avoid #

• Ignoring network latency variations can misrepresent cloud inference performance.
• Testing on inconsistent device states may skew on-device benchmarks.
• Not using identical inputs for both on-device and cloud tests reduces result validity.
• Overlooking privacy implications when transferring sensitive data to cloud.
• Comparing models with vastly different architectures or parameter sizes without normalization.

By following these steps, you can objectively benchmark and compare inference times of on-device versus cloud LLM deployments, enabling data-driven decisions for application design that balance latency, privacy, cost, and user experience.