According to NETINT’s 2026 State of Video Encoding Report, which surveyed 286 video encoding professionals, choosing server hardware for video streaming in 2026 comes down to three decisions: codec, hardware platform, and budget. Meanwhile, 91% of companies already use video in their products and communications, according to Rocketium’s Video Marketing Statistics 2026. That is why server requirements for video streaming are no longer a niche concern for media companies. In this article, the COSMONOVA team explains which parameters determine how many viewers your infrastructure can handle and where configuration choices most often go wrong.
A review of server requirements for video streaming should start with the workload, not the hardware. Live streaming, video on demand, and real-time transcoding place different demands on a server. A configuration that is ideal for delivering a finished file may fail when transcoding four 4K streams simultaneously.
Processor and Computing Power
The processor determines how many concurrent sessions and transcoding jobs a server can handle without a drop in quality. For straightforward content delivery over HTTP, 8–16 cores are sufficient: the network, rather than the CPU, becomes the bottleneck. Real-time transcoding can require dozens of cores, and specialized accelerators outperform CPUs in this area.
The key metric is not clock speed, but throughput per watt and per core. When planning capacity, allow a 30–40% margin: peak workloads during live broadcasts tend to arrive in waves.
RAM and Storage Subsystem
RAM affects the number of parallel streams the server can handle, while the storage subsystem affects startup latency and storage costs. As a practical guideline, allocate 2–4 GB of RAM for each active transcoding stream and 1–2 GB for content delivery.
Two storage parameters are critical: IOPS and throughput. NVMe is useful for segment caching and recording live streams, while high-capacity HDDs or object storage are better suited to archives. Video storage requirements grow quickly: one hour of 4K content can occupy tens of gigabytes even before packaging.
Video Transcoding Server: GPU, VPU, or CPU
In 2026, a video transcoding server is almost always built around a hardware accelerator. CPU-only transcoding remains an option for small workloads and unusual codecs where flexibility matters more than stream density.
In practice, there are three main approaches:
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CPU transcoding. Maximum compatibility and minimal dependencies, but low stream density and high power consumption per stream.
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GPU. Flexible codec and filter support, suitable for complex processing, but more expensive per stream and requires rack space.
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VPU (Video Processing Units). Specialized encoding chips that deliver higher stream density per watt and lower cost per stream for large-scale encoding.
Hardware Acceleration and Codec Selection
Hardware acceleration pays off only when the codec is chosen correctly. H.264 remains the universal compatibility standard, but HEVC and AV1 deliver significantly lower bitrates at the same quality.
The trade-off is straightforward: AV1 saves bandwidth but requires modern accelerators and compatible playback devices. If your audience uses mobile devices from different generations, keep H.264 as the baseline profile and add HEVC or AV1 as a second stream for supported devices.
Pro Tip
In practice, encoding two or three quality levels from the outset is more cost-effective than chasing a single “perfect” codec. Different networks and devices receive the profile that suits them, and you avoid losing viewers using older phones.
Calculating Bandwidth for Video Streaming
Bandwidth planning for video streaming follows this formula: stream bitrate × number of concurrent viewers × safety factor. For 5,000 viewers watching a 4 Mbps stream, outbound traffic reaches 20 Gbps. That capacity is needed continuously, not just during peak periods.
The formula is:
Required bandwidth = (bitrate × number of viewers) × 1.3
A factor of 1.3 accounts for overhead traffic, retransmissions, and uneven peaks. Calculations should focus on outbound traffic (egress), as this is a major cost driver, as noted by Forasoft in Video Streaming Server Cost: The 2026 Four-Line Model.
How Many Viewers Can a Network Handle?
The number of viewers a network can support depends on bitrate, not on the number of “slots” on a server. A 10 Gbps connection can serve approximately 2,500 viewers at 4 Mbps or 1,250 viewers at 8 Mbps.
| Network capacity | 2 Mbps bitrate | 4 Mbps bitrate | 8 Mbps bitrate |
|---|---|---|---|
| 1 Gbps | ~500 viewers | ~250 viewers | ~125 viewers |
| 10 Gbps | ~5,000 viewers | ~2,500 viewers | ~1,250 viewers |
| 40 Gbps | ~20,000 viewers | ~10,000 viewers | ~5,000 viewers |
These figures do not include a safety margin. For real-world planning, subtract 30% for overhead traffic and reserve capacity.
HLS and MPEG-DASH Protocols: What They Mean for the Server
HLS and MPEG-DASH determine how a server splits video into segments and delivers them to the client. HLS uses segments in TS or fMP4 containers and an m3u8 playlist, while MPEG-DASH uses an XML manifest and is often used in browsers through MSE.
For server infrastructure, the practical difference is that HLS is easier to cache on a CDN and is natively supported on Apple mobile devices.
What the Protocol Actually Requires from the Server
From a hardware perspective, the difference between HLS and DASH is not the codec but the request pattern:
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Segment length. HLS with 2–6-second segments generates more small requests per second than DASH with 4–10-second segments. This directly affects IOPS and caching requirements.
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Manifest. During a live stream, HLS updates the m3u8 playlist with every segment. The server must deliver it with minimal latency, without relying on client-side caching.
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Container. Using fMP4 with HLS and DASH allows the same segments to be reused for both protocols, saving storage and simplifying multi-protocol delivery.
Practical takeaway: if you deliver both protocols, store one set of fMP4 segments and generate two manifests. This reduces storage load and simplifies CDN caching.
Quality Metrics That Determine Hardware Selection
Server selection should be based on measurable criteria. The following metrics are directly affected by the configuration:
| Metric | What it measures | Hardware factors |
|---|---|---|
| Time to First Frame (TTFF) | Playback startup speed | Cache IOPS, network latency, segment length |
| Live latency | Delay behind real time | Segment length, transcoding speed, player buffering |
| Packet loss | Delivery quality | Network capacity, redundancy |
| Uptime | Service reliability | Node redundancy, load balancing |
| Rebuffering ratio | Playback smoothness | Bandwidth headroom, adaptive bitrate ladder |
Low-latency live streaming (2–5 seconds) requires short segments and, consequently, a more powerful transcoding node and faster storage. If latency is not critical, as with VOD or webinars where some delay is acceptable, longer segments can reduce hardware costs.
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How the Protocol Affects Configuration Choices
A delivery server must handle thousands of small requests per second rather than heavy computations. A common beginner’s mistake is buying transcoding hardware when only content delivery is needed.
The opposite mistake is using a weak CPU for transcoding and relying entirely on an accelerator. The accelerator speeds up encoding, but frame preparation, segment creation, and container packaging still rely on the CPU. If there are not enough CPU cores, the accelerator sits idle.
Pro Tip
Start with metrics, not hardware. Define your target latency, acceptable TTFF, and availability level, then select segmentation, protocol, and server configuration to meet those targets. This helps you avoid paying for processing power that does not improve the user experience.
Comparing Server Configurations for Different Workloads
Below are three typical configurations for different scenarios. They are guidelines rather than a ready-made price list: specific models and costs depend on the facility and workload.
Image: An engineer checking cables and equipment in a server rack, with cool data-center lighting and rows of servers in the background.
| Workload profile | Processor | Memory | Storage | Accelerator |
|---|---|---|---|---|
| VOD delivery for up to 1,000 viewers | 8–16 cores | 32 GB | NVMe cache + HDD archive | Not required |
| Live streaming for up to 10,000 viewers | 16–32 cores | 64–128 GB | NVMe + object storage | 1–2 GPUs or VPUs |
| 4K transcoding across multiple platforms | 32+ cores | 128–256 GB | NVMe array | Several VPUs |
For VOD, cache and bandwidth are the priorities. For live streaming, focus on accelerators and network headroom. For multi-platform transcoding, accelerator density per rack unit matters most.
Scaling, Fault Tolerance, and Total Cost of Ownership
Total cost of ownership consists of four components: outbound traffic, storage, computing, and related services. The model described by Forasoft helps identify hidden expenses such as backup connections, licenses, support, and redundancy.
Three Infrastructure Models: Which One Fits Your Workload?
| Model | Capital expenditure | Operating expenditure | Best suited for | Main risk |
|---|---|---|---|---|
| Owned hardware in a colocation facility | High, one-time | Moderate: bandwidth, electricity, support | Stable, round-the-clock workloads and sensitive data | Hardware downtime and depreciation over 3–5 years |
| Dedicated server rental | None | Predictable, fixed | Live streams with a known schedule | Limited configuration flexibility |
| Cloud | None | Variable, traffic-dependent | Peak workloads and irregular streams | Egress traffic can consume the budget as the audience grows |
A practical rule: if outbound traffic consistently exceeds 10 Gbps per day, owned infrastructure or a dedicated server is almost always cheaper than cloud hosting over a 12–24-month period. If the workload fluctuates, cloud services can be more cost-effective because you pay for peak capacity only when needed.
Vertical and Horizontal Scaling
Vertical scaling—adding more cores, memory, and accelerators to a single node—is limited by rack space and cost. A top-tier CPU with several VPUs can become disproportionately expensive. Horizontal scaling—adding multiple identical nodes behind a load balancer—supports linear growth and provides natural redundancy, but requires HLS/DASH segments to be accessible to all nodes through shared storage or a CDN.
For VOD delivery, horizontal scaling is standard: add a node and increase bandwidth. Real-time transcoding is more complex because streams must be distributed across nodes and quality ladders synchronized.
Failure Handling: What You Should Actually Test
Fault tolerance relies on N+1 redundancy and geographically distributed nodes. For media companies, this is not a luxury: a single node failure during a live broadcast can damage their reputation.
Do not just check whether backup systems exist. Check how quickly failover occurs:
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Load balancer. Active-passive configurations switch over in seconds. Active-active configurations can switch in fractions of a second but require session-state synchronization.
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Storage. Segment replication between facilities must fit within the segment duration, typically 2–6 seconds, or viewers may experience playback interruptions.
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Network connections. At least two independent providers with automatic BGP failover are needed for live streaming.
Warning
The most expensive mistake is considering hardware costs alone. Without a backup network connection and storage replication, the first network failure can wipe out the savings from skipping redundancy. The second costly mistake is underestimating egress traffic: as the audience grows, it can exceed all other expenses combined.
Hybrid Architecture as a Compromise
Hybrid architecture has become one of the main approaches in 2026. Unified Streaming identifies efficiency and cost control as key market drivers and points to a combination of cloud services and secure, privately owned data centers. In practice, peak workloads move to the cloud, while steady traffic and sensitive data remain on dedicated hardware.
A typical setup uses owned nodes for baseline traffic and cloud instances for spikes during live broadcasts or product launches. These instances can start on a schedule or in response to a trigger. This means you pay for cloud capacity only when it is actually needed.
How to Choose Server Hardware for Video Streaming
Start with three questions: how many concurrent viewers do you expect, what bitrate do you need, and is real-time processing required? The answers determine everything else.
Step-by-step:
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Calculate peak concurrent viewers and multiply by the bitrate, adding a 30% safety margin.
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Decide whether you need a transcoding node or only a delivery server.
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Select an accelerator for the codecs your audience actually supports.
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Allocate storage for archives and cache capacity for segments.
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Check the network infrastructure: bandwidth, backup connections, and attack protection.
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Plan redundancy and geographic distribution of nodes.
The facility matters just as much as the calculations. Hosting a server in a data center with direct connectivity to telecom operators helps address latency and peak-traffic issues. COSMONOVA provides data centers in Kyiv and Europe, reliable internet connectivity, scalable cloud infrastructure, and integrated solutions for television and video processing.
Conclusion
Server requirements for video streaming come down to one simple principle: calculate the workload first, then choose the hardware. Miscalculating bandwidth or selecting the wrong codec can cost more than paying extra for an accelerator.
If you need infrastructure that can keep live streams running without interruptions, COSMONOVA offers secure data centers, redundant internet connections, and cloud solutions with technical support. We can help design a configuration tailored to your actual workload.
Frequently Asked Questions
What server specifications are needed for video streaming?
Four parameters are critical for video streaming: a processor that supports hardware acceleration, enough RAM to buffer streams, a fast storage subsystem for storing and delivering content, and sufficient network bandwidth. According to NETINT’s 2026 State of Video Encoding Report, which surveyed 286 specialists, key considerations when choosing server hardware include comparing GPU and VPU performance and adopting AI in encoding workflows. For basic delivery without transcoding, a CPU and NVMe drives are sufficient.
Is a GPU necessary for video transcoding on a server?
A GPU or VPU is needed when the server performs real-time transcoding. NETINT’s 2026 report shows that specialists increasingly compare GPUs and VPUs when selecting equipment. A CPU is sufficient for simple delivery of pre-encoded files through HLS or MPEG-DASH. If you convert streams into multiple bitrates for different devices, hardware acceleration reduces CPU load and allows a single machine to serve more concurrent viewers.
How do you calculate network bandwidth for video streaming?
Bandwidth planning depends on three variables: stream bitrate, the number of concurrent viewers, and headroom for peak loads. Multiply the bitrate by the number of viewers, then add 30–50%. For example, 5 Mbps for 1,000 viewers requires 5 Gbps before adding headroom. Forasoft’s 2026 cost model treats outbound traffic as a separate expense, so account for egress in advance. Use a CDN to distribute the load.
How do server requirements differ for live streaming and video on demand?
Live streaming requires minimal latency and a stable signal input, so the server needs a fast network interface and enough computing power for real-time transcoding. Video on demand (VOD) allows batch processing: files are encoded in advance, and the server focuses on storage and delivery. According to Grand View Research, the video streaming market is valued at $191.1 billion in 2026. The growth of live streaming is encouraging operators to adopt hybrid architectures that combine cloud services with owned data centers.
