Edge Latency & Spatial Bandwidth Calculator | MEC Tools

Q: Why can't I just process video streams in AWS/GCP?

Bandwidth costs and reliability. Streaming raw video 24/7 across hundreds of cameras requires immense uplink bandwidth which is extremely costly. Furthermore, if the internet connection drops, the security system goes completely blind. Edge AI ensures continuous operation.

Q: Is Wi-Fi 6 fast enough for wireless PC-VR?

Yes, provided the router is in line-of-sight and the network is uncongested. Wi-Fi 6 (and especially Wi-Fi 6E on the 6GHz band) can comfortably sustain the 150-200 Mbps of compressed HEVC video required by modern headsets like the Quest 3 while maintaining low latency.

Q: What is 5G Network Slicing?

Network slicing allows operators to partition a single physical 5G network into multiple virtual networks. For example, an autonomous vehicle fleet can be assigned a "slice" guaranteeing ultra-low latency and high reliability, completely insulated from consumer smartphone congestion on the same tower.

Q: What is Multi-access Edge Computing (MEC) and how does it reduce latency?

Multi-access Edge Computing (MEC) deploys compute resources at or near the cellular base station, putting processing power within 1–10 km of the end device instead of hundreds of km away in a central cloud. This cuts round-trip latency from 50–150ms (cloud) to 1–10ms (edge), which is essential for AR/VR, autonomous vehicles, and real-time industrial control. 5G networks are purpose-built to leverage MEC through network slicing and ultra-low-latency radio protocols.

Q: What frame rate is required for comfortable AR/VR experiences?

Comfortable VR requires a minimum of 72 FPS to avoid motion sickness, with 90 FPS being the current standard for headsets like Meta Quest and PlayStation VR2. High-end PC VR runs at 120–144 FPS. For AR passthrough (showing real-world video), the camera and rendering pipeline must match at 60 FPS minimum to prevent a disorienting "swimming" effect. Latency from sensor input to displayed frame (motion-to-photon latency) must stay under 20ms regardless of frame rate.

Q: How much bandwidth does an IoT sensor fleet actually need?

It depends entirely on data rate and polling frequency. A temperature sensor sending 10 bytes every 30 seconds requires only ~2.7 bps per device. However, a fleet of 10,000 sensors simultaneously bursting data creates 27 kbps of aggregate traffic. Video sensors are the extreme case — a single 1080p camera at 30fps requires 2–8 Mbps. For large IoT deployments, always calculate aggregate bandwidth and account for 2–3× bursty headroom in your network design.

Q: What is the difference between edge computing and fog computing?

Edge computing places processing directly on or near the device or sensor (e.g., on a smart camera or gateway). Fog computing is a broader Cisco-coined term that describes a distributed architecture spanning multiple layers between devices and the cloud, including local gateways, on-premises servers, and regional data centers. In practice, the terms are often used interchangeably. The key distinction is that edge is the closest possible layer to the data source, while fog encompasses the entire distributed hierarchy.

Q: Why can't I just process video streams in AWS/GCP?

Bandwidth costs and reliability. Streaming raw video 24/7 across hundreds of cameras requires immense uplink bandwidth which is extremely costly. Furthermore, if the internet connection drops, the security system goes completely blind. Edge AI ensures continuous operation.

Estimate end-to-end edge compute latency, calculate AR/VR spatial bandwidth requirements, and analyze IoT fleet economics instantly.

Edge Latency Calculator

Use this free Edge Latency Calculator to run instant, privacy-first client-side calculations.

End-to-End Latency Metrics (RTT)

Propagation Delay (RTT)

0 ms

Hard physics limit (Speed of Light)

Routing Delay

0 ms

Router/Switch queueing & switching

Total Round Trip Time

0 ms

VR Target: < 20ms RTT

Spatial Bandwidth Calculator

Use this free Spatial Bandwidth Calculator to run instant, privacy-first client-side calculations.

AR/VR Bandwidth

Resolution Per Eye

Refresh Rate (Hz)

Color Depth (bits per pixel)

Foveated / Compression Ratio

(Ratio of 20 means 20:1 compression)

Streaming Requirements (Both Eyes)

Uncompressed Raw

0 Gbps

Hardwired DisplayPort needed

Compressed Output

0 Mbps

Over-the-air payload

Minimum standard

Wi-Fi

Recommended wireless tech

Data Reduction Impact

CV Inferencing Calculator

Use this free CV Inferencing Calculator to run instant, privacy-first client-side calculations.

Compute Hardware Requirements

Required TOPS

0 TOPS

Tera Operations Per Second

Pixels Processed / Sec

0 Mpx/s

Across all parallel streams

Recommended Hardware

Jetson

Suggested Edge AI Node

IoT Fleet Econ Calculator

Use this free IoT Fleet Econ Calculator to run instant, privacy-first client-side calculations.

Fleet Economics (36 Months)

Raw Cloud Cost / Mo

Without Edge Processing

Edge-Optimized Cost / Mo

Cloud bill after data reduction

ROI Break-Even

0 Months

To pay off Edge HW CapEx

Cold-Start Latency Calculator

Use this free Cold-Start Latency Calculator to run instant, privacy-first client-side calculations.

Cold Start Breakdown

Container Spin-up

0 ms

Network / App Init

0 ms

Total Cold Start Penalty

0 ms

Added delay for the first request after scaling from zero

Gaussian Splat Calculator

Use this free Gaussian Splat Calculator to run instant, privacy-first client-side calculations.

3D Gaussian Rendering VRAM

Model Storage Size

0 MB

Frame Budget

0 ms

Est. VRAM Required

0 GB

Splat buffers + sorting overhead

LiDAR Bottlenecks Calculator

Use this free LiDAR Bottlenecks Calculator to run instant, privacy-first client-side calculations.

Autonomous Vehicle / Robot Data Rate

LiDAR Throughput

0 Mbps

Vision Throughput

0 Mbps

Total Data Generated (Per Hour)

0 GB / hr

Continuous data logging rate

FHE Overhead Calculator

Use this free FHE Overhead Calculator to run instant, privacy-first client-side calculations.

Fully Homomorphic Encryption Impact

Effective Ops / Sec required

Massive CPU load increase

Encrypted Payload Size

0 GB

RAM and Network bottleneck

Time to Compute (vs 1s Plaintext)

0 Hours

INFRASTRUCTURE ARCHITECTURE

Architecting the Edge

The physics of latency dictate that real-time spatial computing and high-throughput AI inferencing cannot rely solely on the cloud. Explore the constraints of bandwidth, the economics of data gravity, and why processing at the edge is the future.

The Physics of Latency

Latency is bound by the speed of light. In a vacuum, light travels at ~300,000 km/s. In fiber optic cables, the refractive index slows this down to roughly 200,000 km/s.

This means a round trip from New York to London across transatlantic fiber has a hard, unbreakable physics floor of ~55ms—before factoring in a single router hop, switch, or compute processing cycle. For VR, where motion-to-photon latency must be < 20ms to prevent nausea, centralized cloud computing is physically impossible.

Multi-Access Edge Computing (MEC)

MEC pushes cloud computing capabilities to the edge of the cellular network (e.g., at the 5G base station). By processing data literally miles from the user, RTT drops to < 5ms.

Cloud: Infinite compute, terrible latency.
Device: Zero latency, battery constrained.
Edge (MEC): High compute, low latency, unlimited power.

Foveated Rendering & Spatial Bandwidth

Streaming dual 4K displays at 90Hz uncompressed requires over 30 Gbps of throughput—vastly exceeding Wi-Fi 6 and 5G capabilities. Spatial computing requires massive compression.

Uncompressed Gbps = (Horiz × Vert) × 2 Eyes × Hz × ColorDepth Ã· 1,000,000,000

Foveated Rendering utilizes ultra-fast eye tracking to render only the exact focal point of the user's retina at full 4K resolution. The peripheral vision is rendered at drastically lower resolutions, reducing the payload size by up to 90%, making wireless AR/VR streaming viable.

The Economics of Data Gravity

"Data Gravity" dictates that as data masses grow, it becomes increasingly difficult and expensive to move them. Streaming 1000 raw 4K security camera feeds to AWS for processing requires absurd bandwidth and incurs massive ingress/egress fees.

By deploying Edge AI Nodes (like NVIDIA Jetsons) on-site, the inference happens locally. Instead of streaming gigabytes of video, the edge node streams bytes of metadata (e.g., "Person detected at 14:02"). The CapEx of the edge hardware is rapidly amortized by the monthly savings in cloud bandwidth.

Key Architecture Terminology

TOPS

Tera Operations Per Second. A metric of AI hardware performance indicating how many trillion math operations a chip can execute per second. Critical for sizing Computer Vision hardware.

Motion-to-Photon

The time it takes for a user's physical head movement to be reflected in the display. Must remain strictly under 20ms to prevent VR-induced motion sickness.

MACs

Multiply-Accumulate Operations. The fundamental mathematical operation in Neural Networks. Knowing the MACs per pixel helps estimate TOPS requirements.

RTT

Round Trip Time. The latency from the client device, to the server, and back to the client. Includes propagation, transmission, and processing delays.

Frequently Asked Questions

Edge AI & Inferencing

Why can't I just process video streams in AWS/GCP?

Bandwidth costs and reliability. Streaming raw video 24/7 across hundreds of cameras requires immense uplink bandwidth which is extremely costly. Furthermore, if the internet connection drops, the security system goes completely blind. Edge AI ensures continuous operation.

Spatial Computing & AR/VR

Is Wi-Fi 6 fast enough for wireless PC-VR?

Yes, provided the router is in line-of-sight and the network is uncongested. Wi-Fi 6 (and especially Wi-Fi 6E on the 6GHz band) can comfortably sustain the 150-200 Mbps of compressed HEVC video required by modern headsets like the Quest 3 while maintaining low latency.

Network Architectures

What is 5G Network Slicing?

Network slicing allows operators to partition a single physical 5G network into multiple virtual networks. For example, an autonomous vehicle fleet can be assigned a "slice" guaranteeing ultra-low latency and high reliability, completely insulated from consumer smartphone congestion on the same tower.

What is Multi-access Edge Computing (MEC) and how does it reduce latency?

Multi-access Edge Computing (MEC) deploys compute resources at or near the cellular base station, putting processing power within 1–10 km of the end device instead of hundreds of km away in a central cloud. This cuts round-trip latency from 50–150ms (cloud) to 1–10ms (edge), which is essential for AR/VR, autonomous vehicles, and real-time industrial control. 5G networks are purpose-built to leverage MEC through network slicing and ultra-low-latency radio protocols.

What frame rate is required for comfortable AR/VR experiences?

Comfortable VR requires a minimum of 72 FPS to avoid motion sickness, with 90 FPS being the current standard for headsets like Meta Quest and PlayStation VR2. High-end PC VR runs at 120–144 FPS. For AR passthrough (showing real-world video), the camera and rendering pipeline must match at 60 FPS minimum to prevent a disorienting "swimming" effect. Latency from sensor input to displayed frame (motion-to-photon latency) must stay under 20ms regardless of frame rate.

How much bandwidth does an IoT sensor fleet actually need?

It depends entirely on data rate and polling frequency. A temperature sensor sending 10 bytes every 30 seconds requires only ~2.7 bps per device. However, a fleet of 10,000 sensors simultaneously bursting data creates 27 kbps of aggregate traffic. Video sensors are the extreme case — a single 1080p camera at 30fps requires 2–8 Mbps. For large IoT deployments, always calculate aggregate bandwidth and account for 2–3× bursty headroom in your network design.

What is the difference between edge computing and fog computing?

Edge computing places processing directly on or near the device or sensor (e.g., on a smart camera or gateway). Fog computing is a broader Cisco-coined term that describes a distributed architecture spanning multiple layers between devices and the cloud, including local gateways, on-premises servers, and regional data centers. In practice, the terms are often used interchangeably. The key distinction is that edge is the closest possible layer to the data source, while fog encompasses the entire distributed hierarchy.

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