NVIDIA GPU Selector: Find the Right GPU Tier for Your LLM Workload
This free NVIDIA GPU selector scores your workload across eight dimensions, model size, concurrency, latency sensitivity, budget, facility power, and scaling needs, and recommends a GPU tier from entry-level RTX class through flagship B200 multi-node clusters. It is built for engineers and IT leaders who are early in planning an on-prem AI deployment and need a defensible starting point before requesting vendor quotes. Answer eight questions about your actual workload, not your aspirations, and the tool returns a scored recommendation with concrete next steps for that tier.
1. What is your primary workload type?
Training and fine-tuning need more headroom than inference alone, and multi-node work needs fast interconnect.
2. What is the largest active model size you need to run?
For mixture-of-experts models like DeepSeek or Kimi K2, use active parameters per token, not total parameters.
3. How many concurrent requests must one GPU node serve?
4. How latency-sensitive is the workload?
5. What is your budget per GPU?
6. What power and cooling capacity does your facility have available?
This is the constraint most teams discover too late, after hardware has already been ordered.
7. Do you need to scale beyond a single 8-GPU server?
8. How much VRAM headroom do you need for context length and KV cache?
Why GPU selection is not just about model size
Teams default to sizing GPUs from parameter count alone, but concurrency, latency SLA, and facility power constrain the decision just as strongly. A 13B model serving 300 concurrent interactive users needs more aggregate memory bandwidth than a 70B model answering ten batch queries an hour. Facility power is the constraint most often discovered after hardware is already on order: a flagship-tier recommendation is meaningless if the server room has 12 kW of spare capacity and a 20-week electrical upgrade timeline ahead of it.
How the tiers map to real hardware in 2026
Each tier in this assessment corresponds to a real, procurable configuration rather than an abstract score. Entry tier means workstation-class GPUs you can order this week; flagship tier means a multi-month procurement and facility project. Matching your score honestly to the right tier avoids the two most common mistakes: over-buying flagship hardware for a workload that never needed it, and under-buying entry hardware for a pilot that was always going to scale to production concurrency within a quarter.
- Entry tier: RTX 5090 or L40S, single server, standard office or closet power.
- Mid tier: A100 80GB or L40S cluster, one node, light facility upgrade.
- High-end tier: H100 or H200 SXM, 8-GPU node, liquid cooling or rear-door heat exchangers.
- Flagship tier: B200 multi-node with InfiniBand, full data center or colocation build.
What to do with your result
Treat the recommended tier as a starting point for a vendor conversation and a facility assessment, not a final purchase order. Validate the model size and quantization assumptions against your actual candidate models, confirm real electrical and cooling capacity with facilities before committing to a rack density, and benchmark a serving engine like vLLM or SGLang on your real prompts before finalizing GPU count. The single biggest source of over-provisioning we see is sizing for peak theoretical load instead of measured production traffic.
How Netray turns this into a procured cluster
Netray takes teams from this kind of directional assessment through a validated reference architecture, vendor sourcing, and installed hardware for aerospace, defense, and electronics manufacturers running private AI. We benchmark your actual workload on loaner or cloud hardware before committing capital, then manage procurement, facility coordination, and burn-in testing. Engagements typically start with a workload and facility assessment that produces a costed architecture within two to three weeks.
Frequently Asked Questions
How do I score a mixture-of-experts model like DeepSeek or Kimi K2?
Use active parameters per token, not total parameters. DeepSeek V3 has 671B total parameters but only about 37B active per token, which behaves closer to a mid-size dense model for memory bandwidth purposes even though the full weight set still needs to fit in aggregate VRAM across the cluster. Score the model size question based on that active parameter count.
Can I mix GPU tiers in one deployment?
Yes, and it is often the right answer. Many enterprises route simple extraction and classification work to a smaller L40S or A100 tier while reserving H100 or H200 capacity for the hardest reasoning tasks or highest-concurrency interactive workloads. This assessment scores a single workload profile; run it again for each distinct workload if your portfolio is mixed.
What if my facility power score is much lower than my other scores?
Facility power is frequently the binding constraint even when budget and workload point to a higher tier. In that case, plan the facility upgrade as its own project with its own timeline, typically 8-20 weeks for electrical and cooling work, and consider colocation as a bridge that lets you deploy the recommended tier without waiting on your own building.
How often should I re-run this assessment?
Re-run it whenever concurrency, model size targets, or budget change materially, and at minimum before every major procurement cycle. GPU pricing and available generations shift roughly every 12-18 months, and a tier that was flagship two years ago is often mid-tier today, which changes both cost and availability assumptions.
Get a validated GPU architecture and procurement plan built from your actual workload benchmarks, not a generic tier.
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