A large port is valuable only when the application, storage, processor, network path and receivers can use it. Plan the working set and concurrency, benchmark each limiting stage and define operational boundaries. The 40, 100 and 200 Gbps catalogue labels are port classes, not universal transfer or availability guarantees.
Translate the port into a data scale
With decimal units, 40, 100 and 200 Gbps correspond to line-rate arithmetic of 5, 12.5 and 25 GB per second. These are reference conversions before protocol and implementation effects. They describe the scale of the surrounding design, not a measured rate or a promise that an origin will deliver that amount of application content.
For a distribution project, estimate concurrent requests, object sizes and the working set. A service repeatedly delivering a small popular set differs from one streaming uncached archives. Make the assumptions explicit. A large port purchased without those assumptions can leave storage, application or receiver limits untouched while increasing the project's recurring cost.
| Listed port class | Raw decimal conversion | Planning implication |
|---|---|---|
| 40 Gbps | 5 GB/s before overhead | Measure the content and application path |
| 100 Gbps | 12.5 GB/s before overhead | Examine storage, concurrency and processing |
| 200 Gbps | 25 GB/s before overhead | Validate every stage and receiving capacity |
Model the working set and cache behaviour
Identify which content is requested frequently and which must be read from storage on demand. A warm cache can produce a result that a cold start cannot sustain. Test both conditions when they matter to the project. Keep the dataset large enough and realistic enough that the measurement answers the question you actually need to resolve.
Imagine a lawful software-download origin with popular releases and an older archive. Compare the busy release window with normal archive access. Reserve capacity for publishing, checksum generation and backup traffic where those operations overlap. This original scenario provides a planning framework; it does not establish any particular hardware or caching service as included in SwissOctet.
Measure storage independently
Start by measuring the storage operations the application performs. Sequential reads of large files and many small random reads can expose different limits. The fio documentation describes workload parameters that can represent those patterns. Use controlled test files and settings that fit the system, and avoid destructive tests on production data.
Record the storage layout, dataset size and test conditions. A benchmark involving cached data is not equivalent to sustained reads from the underlying devices. Repeat with the application's real content path afterwards. More capacity does not automatically increase delivery rate, and several selected drives do not establish a specific array layout or measured throughput.
Observe processor and queue limits
At high transfer rates, processing and interrupt handling can matter alongside application CPU use. Linux documents receive-side scaling and related mechanisms for distributing network work across queues and processors. Their presence and behaviour depend on hardware and configuration. Use those documents to interpret measurements rather than applying a generic tuning recipe to every server.
Watch whether one CPU, worker or receive queue becomes busy before the aggregate system does. Include encryption, compression and request handling in the test when the production service uses them. An idle average CPU number can hide a saturated component. Change one factor at a time and retain the baseline so the effect is understandable.
Validate the serving application
The server must do more than move bytes through a network benchmark. Validate connections, request parsing, authorisation, content reads and response completion with your intended software. Nginx documents file-transfer and connection controls for its own implementation; other software has different settings. Read the documentation for the actual version you will operate.
Test realistic concurrency and observe tail completion times as well as aggregate output. A large aggregate number can coexist with individual requests that time out. Establish a limit at which the service still behaves acceptably, then leave operating margin rather than aiming to run every component at its theoretical maximum throughout the day.
Compare the actual Transit specifications
SwissOctet's Transit families keep the country Swiss while listing different processor, memory and storage foundations. Read the exact plan: Transit 100 lists SSD storage, while other Transit base specifications list NVMe. Additional disk options have their own labels. Do not let the port class substitute for reviewing the rest of the configuration.
Also confirm availability, traffic terms, administrative responsibilities and the recovery process with the actual service. Unmetered traffic describes a traffic model, not permission for arbitrary activity or an uptime commitment. High-volume workloads need clear operating terms and rights to distribute the content, just as smaller ones do.
Prepare operations before opening traffic
Define who can publish content, change configuration and administer the origin. Protect those interfaces, collect useful operational measurements and set thresholds for overload. Keep a way to reduce traffic or roll back a release if the application behaves unexpectedly. A large interface can amplify the consequences of an ordinary software or access mistake.
Run a staged exercise using authorised receivers and agreed limits, then increase toward the planned workload. Keep the results, unanswered questions and resource headroom in the deployment record. Select a larger port only when this evidence supports it; a complete, measured design is more useful than the largest number available in the catalogue.
Your practical checklist.
- Convert the port class without treating it as payload delivery.
- Model cache hits, cold reads and concurrent requests.
- Benchmark storage with harmless test data.
- Observe CPU, workers and network queues.
- Test the serving application and receiver capacity.
- Stage traffic and retain a rollback procedure.
The details that matter.
Is the largest port always the safest choice?+
No. Choose from measured demand and the complete system's limits. A larger port does not resolve insufficient storage, processor or application capacity.
Does every Transit family use NVMe?+
No. Read the actual plan. Transit 100's base specification lists SSD; other base plans and additional-drive options have their own descriptions.
Why test with cold data as well as cached data?+
They expose different operating conditions. A release, restart or changing working set may produce storage activity that a warm-cache benchmark avoids.
Go to the source.
These references support the technical concepts in this guide. Follow the current documentation before changing a production system.
- Linux kernel: Scaling in the networking stack docs.kernel.org
- fio: Workload and measurement documentation fio.readthedocs.io
- Nginx: HTTP core module nginx.org
Editorial guidance by SwissOctet. Examples are illustrative. Service specifications, available options and contractual conditions must be verified separately.