AMD EPYC vs Intel Xeon

You don’t choose CPUs for bragging rights, you choose them to keep customers happy, revenue steady, and growth predictable. The silicon under your stack decides how many users you can serve at once, how fast pages render, and whether checkout feels instant or sluggish. This guide makes the trade-offs clear so you can match the right platform to the jobs you actually run.

Before we get into architectures and benchmarks, it helps to define what “good” looks like in a hosting context. You care about low tail latency, protective isolation between tenants, and clean scaling when demand jumps. With those outcomes in mind, the processor choice becomes much simpler to evaluate.

Moving from goals to decisions, you’ll see that both AMD EPYC and Intel Xeon can deliver excellent results when they’re paired with the right memory, storage, and power design. The difference is how easily each platform reaches your targets for density, responsiveness, and cost. Keep that lens in view as we walk through each area that drives real-world performance.

You know what results you want: speed, uptime, and room to grow without rewiring everything. To get there, start by framing CPU choice as a business lever rather than a checkbox on a spec sheet. With that framing, the next section explains exactly why CPU selection matters for hosting operations day to day.

Why CPU Choice Matters in Hosting

Data center outages carry significant financial consequences. According to the Uptime Institute’s 2024 Global Data Center Survey, 54% of respondents reported their most recent significant outage cost more than $100,000, with one in five reporting costs exceeding $1 million. This makes CPU choice foundational to uptime and scalability.

CPUs sit at the heart of your capacity model: they bound concurrency, shape database throughput, and gate how many containers or VMs you can safely schedule on each node. When cores are starved or memory bandwidth is thin, no amount of caching can hide the slowdown during traffic spikes. When cores are right-sized and well-fed, you get consistent p95 and p99 latency, even under load.

Every choice downstream benefits from a CPU platform that fits the work. That is why we’ll compare EPYC and Xeon not as abstract chips, but as foundations for your web, eCommerce, SaaS, and enterprise bot workloads. With that context set, let’s look at how each architecture actually behaves.

You’ve seen why the processor decision reaches far beyond raw gigahertz. The next step is understanding what each platform actually brings to the table. With the “why” covered, we can now focus on the “how” by examining architecture and performance traits that show up in your metrics.

Architecture and Performance: Where Each CPU Shines

AMD EPYC: Multi-Core Density and I/O Headroom

EPYC’s signature is high core counts per socket, wide memory channels, and abundant PCIe lanes. In practice, that means you can host more guests per node, keep PHP workers responsive under burst, and attach generous NVMe pools without starving the bus. The platform’s thread scaling tends to hold up as concurrency climbs, which protects both throughput and tail latency.

For agencies packing many client sites per server, this density translates into better node utilization and calmer p95 times during content pushes. For analytics jobs, background queues, and ETL, parallel work finishes sooner so foreground traffic stays snappy. When you need storage performance to match CPU capacity, the extra PCIe lanes give you clean paths for NVMe and fast networking.

The throughline is simple: EPYC makes it easier to maintain performance as you add workloads, not just when the node is empty. That steadiness is exactly what multi-tenant platforms need. With those strengths in mind, let’s contrast them with Xeon’s per-core profile.

EPYC’s story is about scaling gracefully as concurrency rises. Still, some applications are less about many threads and more about fast single-thread response. To cover that side of the spectrum, we should look at what Intel Xeon does well and why it continues to anchor many enterprise stacks.

InMotion Hosting recently introduced its Extreme Dedicated Server plan featuring the AMD EPYC 4545P processor with 16 cores and 32 threads. Paired with 192GB of DDR5 ECC RAM and dual 3.84TB NVMe SSDs, this configuration delivers the core density and memory bandwidth that EPYC is known for. The plan also includes burstable 10Gbps bandwidth, making it a strong fit for streaming workloads, high API volume, and large CRM deployments where traffic can spike without warning.

Intel Xeon: Per-Core Speed and Ecosystem Familiarity

Xeon’s advantage is strong per-core performance and a deep ecosystem of validated drivers, management tools, and certified integrations. If your traffic pattern leans transactional, fast cores help keep p99 low. And when you rely on commercial software with strict certification paths, Xeon often means fewer surprises.

That maturity shortens setup time for teams with historic Xeon playbooks and vendor-specific tooling. It can also simplify audits when you operate in regulated sectors or run specialized HBAs, NICs, or RAID controllers. The net effect is faster time-to-steady-state for stacks that prioritize per-core speed and vendor assurances.

Neither approach is “better” in the abstract; each is better for certain patterns. The right match depends on whether your bottleneck is threads, memory bandwidth, or request path latency. To make that even clearer, we’ll translate these traits into common hosting patterns you likely run.

For workloads that demand maximum per-core speed or high multi-core density, dedicated servers with either AMD EPYC or Intel Xeon give you the full benefit of the architecture.

Architecture is useful only because it changes how your workloads feel. You’ve now seen where EPYC tends to scale and where Xeon tends to respond fastest. Next, we’ll zoom in on threading because those features determine how nodes behave when everyone hits you at once.

SMT vs HT: Threading Efficiency You Can Feel

Simultaneous Multi-Threading (SMT on AMD) and Hyper-Threading (HT on Intel) allow two threads to share one physical core. When threads would otherwise stall on memory or I/O, the sibling can keep the pipeline busy. The result, when tuned well, is higher throughput per watt and better behavior under burst.

EPYC’s SMT often benefits from the platform’s memory bandwidth and lane counts, which let threads stay fed as concurrency rises. Xeon’s HT delivers solid wins for web and API traffic where per-core speed is already strong, smoothing spikes without re-sharding your app. In both cases, threading efficiency decides how far you can safely push vCPU allocation without harming neighbors.

For hosting, that translates into VPS density, steady p95 at busy hours, and cleaner cost curves. It also matters for real-time systems like enterprise Discord bots, where thousands of events can land simultaneously. With threading covered, the next limiter to check is how quickly data can reach the cores.

Threads only help if memory and storage keep up. If channels choke or PCIe saturates, extra threads won’t move your metrics. That’s why the next section focuses on memory bandwidth and I/O, two areas that quietly decide how fast your stack can go.

Memory, I/O, and Storage: The Unsung Heroes

Memory bandwidth is the oxygen of modern workloads. EPYC platforms typically expose more memory channels per socket, which helps databases, PHP workers, and analytics tasks that move a lot of data. Faster DDR speeds amplify that benefit when you’re memory-bound rather than compute-bound.

PCIe lanes decide how many NVMe drives and high-speed NICs you can run without contention. With more lanes, you can separate storage, replication, and backup paths so queue depth stays low during peak writes. That keeps your database responsive and your caches fast, even when batch jobs are busy.

When CPU, memory, and I/O are balanced, the system feels effortless to your users. If one lags, the others wait, and your p95 latency tells the story. With performance tunables in view, we should address another first-order requirement for multi-tenant platforms: security and isolation.

Fast is necessary, but safety is non-negotiable. When many customers share a node, you need hardware-level guardrails. The next section explains how EPYC and Xeon handle isolation and encryption so you can align features to your risk model.

Security and Workload Isolation

AMD EPYC offers Secure Encrypted Virtualization (SEV and SEV-SNP) to encrypt guest memory and isolate VMs at the hardware layer. According to AMD’s official documentation, SEV-SNP adds strong memory integrity protection to help prevent malicious hypervisor-based attacks like data replay and memory re-mapping. In practice, that’s a strong fit for agencies and SaaS platforms that run many tenants per node and want defense-in-depth without rewriting apps. It’s also a practical way to raise the bar for cross-tenant risk.

Intel Xeon provides Intel SGX for secure enclaves and Total Memory Encryption (TME) for full-memory encryption. According to Intel’s newsroom, TME helps ensure that all memory accessed from the Intel CPU is encrypted, including customer credentials, encryption keys, and other sensitive information on the external memory bus. SGX is useful for protecting specific secrets or sensitive routines, especially in financial or healthcare contexts where enclave patterns are already part of the architecture.

Both approaches improve your security posture; the right choice depends on how your workloads are built and audited. If your main exposure is between tenants, EPYC’s VM-level isolation maps well to real hosting. If you need enclave-style protection and vendor-specific attestations, Xeon aligns with that path. With safety covered, let’s connect performance and protection to the uptime story.

Security reduces risk, but uptime protects revenue. Power design is where those two meet in the real world. The next section shows how redundant, efficient PSUs support your 99.99% target while lowering operating costs.

Power, Redundancy, and Uptime: The PSU Link You Can’t Ignore

Your CPU platform can’t help you if a single power fault brings the node down. In server environments, redundant, hot-swappable PSUs (ideally 80 PLUS Platinum or Titanium) are the standard for continuous operation. Dual or N+1 designs let you replace a failed unit without downtime and share load to extend lifespan.

Testing by ServeTheHome on HPE ProLiant 800W PSUs demonstrates that Platinum-rated units at 230V achieve 94% efficiency at 50% load, while Titanium-rated units reach 96% efficiency. Running two 800W PSUs at a combined 400W load keeps both units in their optimal efficiency band.

Efficiency matters because it compounds across a fleet. Higher-efficiency PSUs waste less energy as heat, which reduces electricity and cooling costs; even modest per-server savings add up to thousands annually at scale. Aim to run PSUs at 50–80% of capacity and size with 20–30% headroom so spikes don’t push you into inefficient ranges.

This is where EPYC’s performance per watt can lower node counts for a given SLA, and where Xeon builds still benefit from the same power discipline. Either way, power planning is a core part of delivering a true 99.99% experience. With the box reliable, the next question is how to keep adding capacity smoothly as demand grows.

Stability is the baseline; scalability is the differentiator. When traffic jumps, you want to add the resource that fixes the bottleneck, not random hardware. The next section explains hyperscale in simple terms so you can scale with intent.

Hyperscale: How You Meet Demand Without Breaking Things

Hyperscale is the ability of your platform to add the right resource at the right time without drama. If the CPU is pegged, you add cores or nodes; if I/O is the issue, you add NVMe or lanes; if memory is the limiter, you add channels or faster DDR. Scaling only works if you fix the actual bottleneck.

EPYC’s density and lane counts make horizontal growth straightforward in VM and container clusters. Xeon’s ecosystem depth helps when your hyperscale plan depends on certified vendor tooling and long-standing integrations. Both can be excellent bases for autoscaling, provided you align resources with demand signals.

Your readers don’t have to become capacity planners to benefit from the idea. They just need to know that scaling works best when measured against p95/p99 latency, queue depth, and saturation of specific subsystems. With the scaling model set, let’s apply everything to real-world hosting scenarios.

All this theory is only helpful if it maps to the work you do every day. The following use cases show how CPU choice changes density, latency, and risk in common hosting setups. Use them as templates when you spec your next node.

Real-World Hosting Scenarios

1. Agencies Hosting Hundreds of Client Sites (EPYC-leaning)

Why EPYC fits: High core counts and wide memory bandwidth help maintain steady TTFB as you add sites per node. Abundant PCIe lanes make NVMe pools and fast NICs straightforward, which keeps database reads quick when editorial teams push content. SMT efficiency helps absorb bursty traffic across many small tenants.

What to do: Right-size PHP workers to physical cores and SMT threads, set per-tenant limits to protect neighbors, and place logs/backups on separate storage paths. Add read replicas for popular content types and monitor queue depth and p99 latency during marketing events. Keep 20–30% power headroom so you don’t drift into inefficient PSU ranges.

Expected outcome: More customers per node with stable p95 times and predictable scaling decisions. That combination improves margin without trading away user experience.

Agencies care about density, but stores care about checkout speed. Transactional steps stress different parts of the stack than blog posts and CMS updates. That’s where Xeon’s per-core profile can help.

InMotion’s Extreme plan aligns well with this use case. The AMD EPYC 4545P delivers 32 threads for handling concurrent connections, while DDR5 ECC RAM provides the memory bandwidth that analytics and caching layers demand. Burstable 10Gbps bandwidth absorbs traffic spikes without throttling, and 32 dedicated IPs support multi-tenant architectures that require IP isolation.

2. eCommerce with Spiky Checkouts (often Xeon-leaning)

Why Xeon fits: Strong per-core speed benefits synchronous, transaction-heavy paths like cart updates, payments, and fraud checks. With the right storage layout, you can keep write latency low enough to avoid queue buildup. The enterprise ecosystem also helps when you depend on vendor-certified modules.

What to do: Allocate big page caches, shard hot tables when sensible, and put NVMe mirrors on dedicated PCIe lanes. Use rate limiting and queueing to protect p99 during promotions, and instrument the slowest endpoints. Keep TLS and image work off the hot path where possible.

Expected outcome: Lower tail latency through the transaction steps customers feel the most, especially during peak events. The result is fewer abandons and steadier revenue.

Some workloads mix many tenants, steady background jobs, and event spikes. That’s typical of SaaS platforms, where isolation and scale share the stage. Here, EPYC’s thread scaling pairs well with hardware-level isolation.

3. SaaS Multi-Tenant Platform (EPYC-leaning)

Why EPYC fits: SEV/SEV-SNP aligns with multi-tenant isolation at the VM level, and thread scaling smooths concurrency spikes. Memory bandwidth helps analytics and reporting jobs finish without starving request workers. PCIe abundance makes NVMe and fast networking easy to attach cleanly.

What to do: Add Redis for hot data, place background queues on NVMe, and set per-tenant CPU caps. Use read replicas to offload BI queries, and monitor noisy-neighbor patterns with clear remediation rules. Keep failover paths and redundant PSUs ready to maintain 99.99% targets.

Expected outcome: Predictable performance for tenants as you grow, with lower risk of cross-tenant impact. You get scale and isolation without constant firefighting.

Community platforms act like SaaS but face sharper bursts. Enterprise Discord bots are a good example; thousands of users can trigger actions in seconds. That’s a perfect place to combine high thread counts with fast storage and resilient networking.

4. Enterprise Discord Bot Cluster (EPYC core; selective Xeon where latency wins)

Why EPYC fits: Bots serving 5,000–10,000+ active users benefit from many cores and threads; SMT helps with concurrent events. NVMe keeps queues and job logs quick, and extra PCIe lanes support fast NICs for low RTT. If a single microservice needs absolute per-core speed, a small Xeon segment can handle it.

What to do: Run multiple instances behind a load balancer, use PostgreSQL with read replicas, and add Redis caching for hot keys. Deploy across regions to hit sub-50 ms targets and use autoscaling tuned to event rates. Wrap it all with redundant PSUs and DR drills so failovers are routine, not rare.

Expected outcome: Smooth reactions during community surges and predictable costs as the audience grows. Your brand looks reliable because the infrastructure is built that way.

Now that you’ve seen how the CPUs map to real work, the next question is what happens as AI creeps into more parts of your stack. Training and inference pull in different directions. The next section breaks down who’s best where and why.

AI and Emerging Workloads

Training and batch analytics favor parallelism and memory bandwidth. EPYC’s core counts and channels play well here, finishing heavy jobs faster and using fewer nodes for the same work. That saves power and shortens the window where background jobs might compete with user traffic.

Low-latency inference is more nuanced. If the model is modest and runs synchronously inside the request path, Xeon’s per-core speed can help you hit tight p99 targets. If the workload is off the hot path or batched, EPYC’s thread scaling can make better use of hardware during bursts.

Most teams blend approaches: EPYC for the heavy lifting and Xeon where a vendor integration or single-thread path dominates. The key is to profile on realistic inputs rather than assume one pattern or the other. With the AI dimension in place, it’s time to talk about planning for tomorrow without overbuying today.

Future-proofing is not about guessing the future; it’s about reducing regret. You want options without committing to massive rebuilds. The next section shows how each platform supports upgrades, vendor tooling, and long-term stability.

Scalability and Future-Proofing

EPYC Advantages

High core density means fewer physical nodes to reach a concurrency target, and extra PCIe lanes simplify NVMe growth without reshuffling. Consistent socket strategies across generations reduce the number of disruptive rebuilds you face over a platform’s life. That steadiness pairs well with hyperscale strategies that add precise resources as needed.

Xeon Advantages

A deep vendor ecosystem, certifications, and familiar tooling can compress project timelines, especially in audited environments. If you rely on specific HBAs, RAID firmware, or commercial software validated first on Xeon, you’ll spend less time proving compliance or chasing odd driver issues. That predictability can be worth more than a few percentage points on power or throughput.

Both paths can be right. Your best choice lines up with your roadmap, the software you run, and the audits you face. With direction set, the last piece is cost. You’ll want this measured across power, licenses, and the nodes you actually need.

Budgets settle every debate. When you count energy, node count, and time to deploy, the right answer often picks itself. The next section gives you a plain way to compare total cost and avoid surprises.

Cost, TCO, and Energy

More efficient threading and higher core density reduce the number of nodes needed to hit your SLA. Fewer nodes mean lower power draw, fewer OS instances to patch, and smaller licensing footprints where fees are per socket. Pair that with 80 PLUS Platinum/Titanium PSUs and 20–30% headroom to land in the highest efficiency band under typical load.

EPYC often delivers better performance per watt for multi-threaded and mixed workloads, which can move your spend meaningfully over a year. Xeon can lower time-to-value when vendor certification shortens deployment and reduces integration grind. The correct comparison is “cost per unit of business outcome,” not simply “CPU price.”

To keep it practical, calculate the throughput you need at your target p95, estimate nodes for each platform based on measured tests, and multiply by power and licensing. You’ll see the slope of each option immediately. With costs framed, let’s wrap up with a simple sizing workflow you can run before you buy.

You’ve got the principles; now you need a quick process to apply them. A short checklist helps you avoid under-buying or overspending. The next section gives you a repeatable way to size and validate with small tests.

Step-by-Step: Match Your Workload to the Right CPU

Choosing between AMD EPYC and Intel Xeon does not need to be complicated. The key is gathering real data from your workloads and letting the numbers guide your decision. This five-step process helps you avoid over-buying hardware you don’t need or under-specifying servers that will struggle under load.

Step 1: Baseline Your Current Load

Before you evaluate any hardware, you need to know what your workload actually demands. Guessing leads to servers that are either overprovisioned (wasting money) or underprovisioned (frustrating users).

Start by capturing your peak requests per second or events per second during your busiest periods. Look at traffic from the past 30 to 90 days and identify the highest sustained load, not just momentary spikes. If you run an eCommerce site, your baseline might come from a flash sale or holiday weekend. For a SaaS platform, it might be the last hour of the business day when users rush to finish tasks.

Next, establish your latency targets. Most teams track p95 and p99 latency, which represent the response time experienced by the slowest 5% and 1% of requests. A p95 of 200ms means 95% of your users see responses faster than that threshold. If your current p95 is 180ms and your target is 200ms, you have a 20ms buffer. If your p95 is already at 250ms, you need faster hardware or architectural changes.

Finally, identify your bottleneck. Run your monitoring tools during peak load and determine whether you are CPU-bound, memory-bound, or I/O-bound. A CPU-bound workload will show processors pinned near 100% while memory and disk have capacity to spare. A memory-bound workload will show high memory utilization or swap activity even when CPU usage is moderate. An I/O-bound workload will show disk or network queues backing up while CPU cycles go unused. Knowing your bottleneck tells you which hardware specs matter most for your situation.

Step 2: Pick a Sizing Unit

A sizing unit gives you a single metric to compare hardware configurations. Without one, you are guessing. With one, A/B tests on hardware become straightforward.

For web apps, measure requests per second (RPS) where p95 latency stays at or below your target. If your checkout page needs to respond in under 200ms for 95% of users, your sizing unit becomes “RPS at p95 ? 200ms.” Test each platform and record how many requests it handles before latency climbs past that threshold.

For data jobs, measure rows processed per second while keeping CPU utilization under a safe ceiling. A sizing unit like “10,000 rows/sec at ? 70% CPU” tells you the platform can handle your ETL batch with headroom to spare. If you are maxing out at 95% CPU to hit that throughput, you will have no capacity left when the job runs alongside production traffic.

For bots, measure events per second while staying within API rate limits. Discord and Slack enforce strict rate limits, so raw throughput matters less than sustainable throughput. Your sizing unit might be “1,200 events/sec without triggering rate limit backoff.” A platform that processes faster but trips rate limits constantly will feel slower to users than one that stays just under the threshold.

Step 3: Test Small on Both Platforms

Run short, controlled tests on EPYC and Xeon configurations before committing to a full deployment. A few hours of benchmarking can save months of regret.

Keep your test environment consistent. Use identical NVMe drives, the same amount of RAM, and matching network interfaces on both platforms. If one server has faster storage or more memory, your results will reflect that difference rather than the CPU performance you are trying to measure.

Toggle SMT (on AMD) and Hyper-Threading (on Intel) during your tests to see how each platform scales with threading enabled versus disabled. Some workloads benefit significantly from the additional threads, while others see minimal improvement or even slight degradation. Understanding your workload’s threading behavior helps you predict how the server will perform as you add more concurrent users.

Log power consumption during your tests if your infrastructure supports it. Many servers expose power data through IPMI, and most data center PDUs can report per-outlet usage. Capturing this data lets you calculate performance per watt, which becomes important when you scale to multiple nodes or negotiate colocation contracts.

Keep tests short but realistic. A 30-minute test with production-like traffic patterns will teach you more than a 4-hour test with synthetic load. Use realistic database sizes, actual user session behavior, and genuine API payloads whenever possible.

Step 4: Decide with Numbers

Once testing is complete, compare your results across three dimensions: throughput per core, p95 latency at your target load, and energy consumption per unit of work.

Throughput per core tells you how efficiently each platform uses its processing power. If an EPYC server with 64 cores handles 50,000 RPS while a Xeon server with 32 cores handles 30,000 RPS, the Xeon is actually more efficient per core (937 RPS/core versus 781 RPS/core). That might matter if your workload scales better vertically than horizontally.

Latency at target load reveals how each platform behaves under pressure. A server that posts excellent throughput numbers but crosses your p95 threshold 10% sooner than the alternative will cause user-facing problems before the other option would.

Energy per unit of work translates directly to operating costs. If both platforms meet your performance requirements, the one that uses less power to do so will cost less to run over the server’s lifespan. Multiply the difference by your electricity rate, your PUE (power usage effectiveness), and your expected server lifetime to see the real savings.

Pick the platform that meets your SLA with the fewest nodes and the simplest operational story. A slightly faster server that requires custom kernel tuning and exotic driver versions will cost more in engineering time than a marginally slower server that runs reliably with default configurations.

Step 5: Lock Power and Uptime Design

Hardware selection does not end with the CPU. Your power and redundancy design determines whether the server actually delivers 99.99% uptime or just looks good on a spec sheet.

Choose redundant power supplies in an N+1 configuration, meaning you have one more PSU than required to run the server at full load. Dual PSUs sharing a 400W load will each run at roughly 50% capacity, which lands them in the most efficient operating range for 80 PLUS Platinum and Titanium units. If one fails, the remaining PSU takes the full load without interruption while you schedule a replacement.

Size your PSUs with 20 to 30% headroom above your measured peak draw. This buffer keeps the units in their efficient band during normal operation and provides capacity for unexpected load spikes. Running PSUs at 90% or higher pushes them into less efficient ranges and accelerates component wear.

Map your failover paths before you need them. Document which services fail over to which backup systems, how long the failover takes, and what manual steps (if any) are required. Run DR tests quarterly at minimum to verify that your documentation matches reality. A failover plan that has never been tested is not a plan; it is a hope.

Treat power and recovery as first-class features of your infrastructure, not accessories you bolt on after deployment. A server that delivers excellent benchmarks but cannot survive a PSU failure is not production-ready.

Configuration Examples You Can Reuse

High-Throughput API or Streaming Node (AMD EPYC)

CPU: AMD EPYC 4545P (16 cores/32 threads)

RAM: 192GB DDR5 ECC for fast memory access

Storage: 2×3.84TB NVMe SSD in RAID configuration

Network: Burstable 10Gbps with option for dedicated bandwidth

IPs: 32 dedicated IPs for multi-tenant or CDN edge deployments

High-Density VPS Node (EPYC-leaning)

CPU: EPYC with high core count

RAM: Sized to avoid swapping under burst

Storage: NVMe pool on separate PCIe lanes

Network: 10/25GbE with backup traffic isolated

Power: Dual hot-swap PSUs, 80 PLUS Platinum, with 20–30% headroom

Transaction-Heavy eCommerce Node (often Xeon-leaning)

CPU: Xeon with strong per-core clocks

RAM: Sized for large page cache

Storage: NVMe mirrors for fast writes; replicas for reads

Network: Low-latency NICs; keep TLS/image work off the hot path

Power: Redundant PSUs sized for promotional surges

Enterprise Discord Bot Cluster (EPYC core + selective Xeon)

CPU: EPYC for worker pool; optional small Xeon slice for latency-critical microservices

Database: PostgreSQL with read replicas

Cache: Redis for hot keys

Network: Multi-region load balancing to hold sub-50 ms

Power: Dual PSUs; DR tested monthly

These builds aren’t rules; they’re accelerators. Adjust them to your code, data model, and latency goals. With practical setups in hand, we can close with the main points you’ll use to brief your team.

You’ve seen how each platform helps different jobs and how to size without guesswork. The last step is to summarize the decision so your team can act. The next section gives you a tight recap and a clear next move.

A Few Last Thoughts

AMD EPYC delivers multi-core density, generous memory bandwidth, and plenty of PCIe lanes. It excels at virtualization, multi-tenant hosting, analytics, and AI training where thread scaling and I/O headroom keep p95 steady. Paired with efficient, redundant PSUs, it can hit SLAs with fewer nodes and lower energy per unit of work.

Intel Xeon provides strong per-core performance and a mature enterprise ecosystem. It’s a smart fit for transactional paths, certified stacks, and teams that benefit from vendor tooling and validation. With the right storage and network layout, it keeps checkout and API paths snappy when you can’t hide latency.

Choose the platform that best fits your dominant bottleneck and growth plan, then validate with small, realistic tests. Wrap the result in hyperscale practices and power designs that support a 99.99% uptime target. That is how your CPU decision becomes a business advantage instead of a science project.

If you’re sizing a new node or planning a migration, bring us your peak metrics and target p95. We’ll help you map the right CPU, memory, storage, and power design to your goals and budget. The result is a plan that holds up on launch day and scales cleanly when your audience grows. Talk with an expert at InMotion Hosting, and we’ll help you figure out the best plan for your team.