TL;DR
- As local AI workloads grow, businesses may need to upgrade their hardware, particularly including extra RAM and GPU accelerator cards
- Upgrading older systems can be less economical than moving to new hardware platforms that combine best-in-class performance with improved power efficiency
- There’s no formal minimum amount of RAM needed for local AI, but AI operations can be very memory-hungry – which is why the latest Dell PowerEdge servers support multiple terabytes of RAM
- PCIe Gen5 and multi-GPU support in the latest Dell PowerEdge servers provides enough bandwidth and performance for enterprise-class AI services
- Lifecycle management lets businesses manage multiple AI servers from one console, for easy deployment and scale-up
AI workloads are moving in-house. Big gains in efficiency, performance and data protection can be attained when AI services are located close to your data, rather than up in the cloud. And research suggests that on-premises AI solutions can be up to 4.1x more cost-effective than public cloud or token-based services (Enterprise Strategy Group, 2025).
However, AI servers have particular demands; they work best when supplied with plenty of GPU power and large quantities of high-bandwidth memory. It may be possible to upgrade legacy servers to handle local AI services, but IT managers need to decide whether it’s better to move to a newer platform that’s designed from the ground up for AI workloads. Doing so ensures you get not only the performance you need today, but headroom to upgrade RAM and GPU density to accommodate future needs.
As local AI becomes central to a growing range of workflows, here are the key questions that every business needs to consider – and the answers you need to make the right decision.
Do AI workloads always require new servers?
Not necessarily – a general-purpose server can easily handle lightweight chatbot and inferencing functions, especially if it’s fitted with an accelerator card and a decent amount of memory. However, as AI workloads grow in complexity and become more widely used across the business, performance becomes an issue. RAM and GPU upgrades can extend a server’s lifespan, but sooner or later, many businesses will find themselves needing more AI compute performance than their existing hardware can support.
Can I upgrade my existing PowerEdge servers for local AI?
You very likely can. Many PowerEdge systems will accept AI accelerator cards and additional RAM – check the technical guide or look up your Service Tag to find out exactly what upgrades a particular model can accept. However, legacy servers will often be limited to PCIe Gen3 or Gen4, and the older DDR4 memory standard, meaning they can’t match the performance of the latest hardware generations.
When does it make sense to move up to the latest PowerEdge platforms?
As you build up your local AI systems, a day may come when your old server can’t support any more memory, or when its previous-generation architecture becomes a bottleneck for performance. At this point a move to a newer platform may be your only way to keep moving forward. However, it can make sense to replace a server well before you reach its limits – see below.
Is it more cost-effective to upgrade or refresh?
Before investing in any hardware expansion for a legacy server, consider whether you might be better off retiring the old hardware and directing the money towards a newer architecture with a longer lifespan ahead of it.
In addition to far greater expandability and AI potential, the latest Dell PowerEdge servers offer exceptional performance per watt, helped by fourth and fifth-generation Intel Xeon Scalable and AMD EPYC 9004/9005 processors. This reduces the total cost of ownership – especially for demanding workloads such as local AI – and may justify early server replacement.
How much memory does local AI actually need?
There’s no hard requirement that AI must have X gigabytes of RAM – it depends on the complexity of your workload and your performance expectations. However, AI tasks are highly memory-intensive, and can benefit from far higher RAM allocations than a typical application server.
In the current hardware generation, two-socket Dell PowerEdge systems can support up to 8TB of RAM depending on configuration, while four-socket models such as the PowerEdge R960 will take up to 16TB. This means they can hold huge AI models and object data sets entirely in memory for the fastest possible processing, or to enable massively parallel operation. And no matter what your requirements, the latest DDR5 technology, as found in current generation PowerEdge servers, ensures the best performance.
What role do GPUs play in local AI deployment?
Don’t be confused: GPU stands for “graphics processing unit”, but these specialist processors aren’t just about 3D visuals. Their highly efficient, massively parallel design is perfectly suited to AI workloads, and for this reason they’re used on server-class accelerator cards for AI training and inferencing.
Dell’s PowerEdge servers are available with a range of GPU cards from NVIDIA and AMD, and support the latest PCIe Gen5 bus, providing up to 64GB/sec of bandwidth – twice as much as older Gen4 cards and slots. Depending on the model, up to eight PCIe Gen5 cards can be installed, providing ample resources for fast local AI. With powerful GPU hardware and a modern architecture, the Dell PowerEdge R6725 achieved a score of 1,115 in independent TPCx-AI benchmark at SF30 scale, indicating enterprise-class performance for both training and inferencing.
How can lifecycle management help?
Dell’s OpenManage Enterprise platform allows for easy management of your whole server fleet, with centralised performance and resource monitoring. It also handles firmware and driver management, not just for the core platform but for accelerator cards and other components too. While this doesn’t directly boost your AI performance, it makes it easy to manage and expand your local AI resources, and track server health for predictable uptime.
If you think Dell PowerEdge is the right call for your business, find out more on the Dell website:
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