New
How are you allocating AI and GPU costs back to teams?
Shared GPU pools and model APIs make showback harder. What allocation model is working?
Evaluation guide
How to evaluate HPC and accelerated compute
This guide is for infrastructure, research computing and AI platform leads buying or renting accelerated compute for training, inference or simulation. It assumes the spend is large enough that a wrong choice stays visible for years, and that facilities, finance and engineering all have a say.
In short
Use this guide to compare GPUs, AI accelerators, HPC systems and GPU clouds on what decides the outcome: performance on your own workloads, software support and portability, committed delivery dates, power and cooling, utilisation and cost per result, and support for the whole system. Then shortlist three to five companies on Techarda and benchmark your own jobs on each.
What’s changing in HPC & Accelerated Compute
Before you compare anyone, it helps to know what has shifted in the market, because it changes which questions matter.
Accelerator choice has widened
Alongside the established GPU platforms, several vendors now sell accelerators designed for training or for inference. Software support decides how much of that hardware you can actually use.
Inference is a buying decision of its own
Running models in production has different needs from training: latency, cost per request and power efficiency. Hardware and services built for inference are now sold separately from training systems.
Power and cooling set the limits
Dense accelerator racks draw far more power than many existing data halls were designed for, and many need liquid cooling. Facilities can be the deciding constraint.
Renting, reserving and buying all compete
GPU clouds, reserved capacity in the large clouds, colocation and owned systems each have a place. Supply has been tight at times, which has made delivery dates and capacity commitments part of the price.
What to judge vendors on
Those shifts shape the criteria below. Each one says why it matters and what to ask, so you can take it straight into a vendor call.
| Criterion | Why it matters | What to ask vendors |
|---|---|---|
| 1Performance on your own workloads | Peak figures and published benchmarks rarely match your models, data sizes and precision choices. | “Can we run our own training or inference jobs on the proposed configuration before we commit, and will you share the results and settings used?” |
| 2Software support and portability | Hardware is only as useful as the frameworks, libraries and tools that run on it. Code tuned for one platform may need work to move. | “Which frameworks and versions are supported and tested, what would we need to change in our code, and how quickly do you support new framework releases?” |
| 3Delivery dates and capacity | A cheaper system that arrives months later can cost more than it saves. Capacity commitments are part of the deal. | “What delivery or availability date will you commit to in writing, and what happens if you miss it?” |
| 4Power, cooling and facilities | Your site may not support the power density or cooling the system needs, and upgrading a facility takes time. | “What power, cooling and floor loading does the configuration need per rack, and does it require liquid cooling?” |
| 5Utilisation and cost per result | Idle accelerators are the most expensive kind. The number that matters is cost per training run, per simulation or per thousand requests. | “What scheduling and sharing tools come with the system, how do comparable customers measure utilisation, and how would you calculate our cost per result?” |
| 6Cluster networking and storage | Large jobs are often limited by the network between accelerators and by storage throughput rather than by the chips. | “Which interconnect and storage do you propose for our job sizes, and how does performance change as we scale to more nodes?” |
| 7Support for the whole system | Clusters fail in the joins between hardware, firmware, drivers and software. Someone has to own the whole problem. | “Who supports a fault that spans hardware, drivers and frameworks, what are the response times, and how are failed parts replaced on site?” |
| 8Data security in shared environments | Training data and model weights are valuable. Rented and shared capacity raises questions about isolation and residency. | “Where will our data and model weights be stored and processed, how is our workload isolated from other customers, and how is data wiped afterwards?” |
Trade-offs to settle early
No vendor does well on every criterion, and some pull against each other. Decide where you stand on these before the demos start, or the demos will decide for you.
Buy or rent
Owning systems is usually cheaper at high, steady utilisation and needs facilities and staff. Renting suits uncertain or uneven demand and costs more per hour.
Established platform or alternative accelerator
The established platforms have the widest software support and the most experienced engineers. Alternatives may offer better price or efficiency on specific workloads, with more porting work and less community help.
Large cloud or specialist GPU cloud
Large clouds bring surrounding services, security tooling and existing agreements. Specialist GPU clouds may offer capacity sooner and simpler pricing, with fewer services around it.
Next generation or available now
Waiting for the next generation can bring better performance per watt, and leaves your teams without capacity in the meantime. Decide how long the project can wait.
Air cooling or liquid cooling
Air-cooled systems fit existing facilities and limit density. Liquid cooling supports denser racks and needs facility changes and new skills.
Red flags
As the answers come back, watch for these. One on its own isn’t a deal breaker, but it deserves a follow-up question in writing.
- Performance quoted only as theoretical peak figures, with no offer to benchmark your workloads.
- No reference customer running jobs of a similar kind and scale.
- Delivery dates given as estimates, with no commitment or remedy.
- Support described as coming soon for frameworks you rely on.
- Quotes that leave out networking, storage, power or cooling work.
- Shared capacity with no clear answer on isolation and data wiping.
Build your shortlist
Start with the workloads and the facilities you have, then use Techarda to narrow the field to three to five companies.
- Describe the main workloads (training, inference, simulation), their size, the frameworks they use and how steady demand will be.
- Check your facilities for power and cooling limits before you talk to vendors. It removes some options straight away.
- Compare like with like: systems vendors against systems vendors, GPU clouds against GPU clouds, chips against chips.
- Read recent stories on Techarda for product launches, supply news and pricing changes, which move quickly here.
- Benchmark your own workloads on each finalist, then check the practitioner questions below and compare side by side.
Companies to consider in HPC & Accelerated Compute
Listed by recent activity on Techarda (what people are reading, following and discussing). The order says nothing about quality, market share or fit for your needs.
Latest HPC & Accelerated Compute stories
Once you have names, recent news is where pricing changes, acquisitions and outages show up first. These are the five newest stories in this category.
What practitioners are asking
News tells you what vendors announced. These open questions show what teams are still trying to work out, with the least-answered first.
New
How are you getting GPU capacity for AI in 2026?
Reserved cloud capacity, neoclouds, on-prem clusters, shared internal pools? What are lead times and utilisation like?
New
How are you preparing the network for AI workloads?
East-west bandwidth, GPU cluster fabrics, data movement between sites: what has changed in your network plans?
Where evidence is thin
A shortlist is only as good as the evidence behind it, and ours is still growing category by category. Here is what’s missing in HPC & Accelerated Compute right now.
20 of 20 companies have no published reviews
If you’ve run one of these in production, a short review helps the next team decide. Reviews are moderated before they appear.
3 questions with no answers yet
An answer from someone who has made the same decision is often more useful than any guide. Share what worked, what didn’t and what you’d check next time.
Answer a HPC & Accelerated Compute questionFrequently asked questions
What should I ask HPC & Accelerated Compute vendors about performance on your own workloads?
Peak figures and published benchmarks rarely match your models, data sizes and precision choices. Ask: Can we run our own training or inference jobs on the proposed configuration before we commit, and will you share the results and settings used?
What should I ask HPC & Accelerated Compute vendors about software support and portability?
Hardware is only as useful as the frameworks, libraries and tools that run on it. Code tuned for one platform may need work to move. Ask: Which frameworks and versions are supported and tested, what would we need to change in our code, and how quickly do you support new framework releases?
What should I ask HPC & Accelerated Compute vendors about delivery dates and capacity?
A cheaper system that arrives months later can cost more than it saves. Capacity commitments are part of the deal. Ask: What delivery or availability date will you commit to in writing, and what happens if you miss it?
What should I ask HPC & Accelerated Compute vendors about utilisation and cost per result?
Idle accelerators are the most expensive kind. The number that matters is cost per training run, per simulation or per thousand requests. Ask: What scheduling and sharing tools come with the system, how do comparable customers measure utilisation, and how would you calculate our cost per result?
New to some of the terms in this guide? The glossary explains GPU cluster, HPC job scheduler, Liquid cooling and Quantum computing.
This guide is written by Techarda editors to our editorial standards. No vendor paid to appear in it or saw it before publication. See our methodology, the trust dashboard or go back to the HPC & Accelerated Compute hub.