Somebody in your company has decided it is time to run AI on your own hardware. Maybe you want a private chatbot that knows your internal documents, an image pipeline for the product team, or a model that reads invoices at scale. And now you are the person comparing GPUs staring at spec sheets full of TFLOPS, VRAM and interconnect acronyms, while every vendor claims to be the fastest.
This guide is written for exactly that moment. It explains, in plain language, what GPU benchmarks actually measure, which numbers matter for AI work and which ones you can safely ignore, and how to work out how much hardware your use case really needs. By the end you will be able to read any GPU spec sheet, compare two cards on your own, and know precisely which questions to ask before you commit a five- or six-figure budget.
What a GPU Benchmark Actually Tells You
A benchmark is a repeatable test: the same task, run under the same conditions, on different hardware. Because the task does not change, the results can be compared fairly card A finished the workload this fast, card B finished it that fast. That is all a benchmark is, and it is the single most useful tool a buyer has.
It matters because a spec sheet alone cannot answer your question. Manufacturers publish theoretical maximums the compute a chip could deliver under perfect laboratory conditions. Real AI work is messier: data has to move in and out of memory, software adds overhead, and different tasks stress different parts of the card. Two GPUs with similar paper specs can behave very differently once they run an actual language model.
This is why workload benchmarks are the numbers worth reading. Instead of an abstract score, they measure the jobs AI hardware actually does all day: generating text with a language model, producing images with a diffusion model, and reading or analyzing images. If your planned use case matches the workload, the benchmark tells you something real.
One honest limitation, right at the start: benchmarks are reference points, not guarantees. Results shift with driver versions, software frameworks, batch sizes and model settings. A serious comparison including ours is built from publicly published benchmark data and will always say so. Treat every number in this guide, and on any comparison page, as orientation rather than a promised outcome.
Reading a benchmark chart: one baseline, proportional bars, and a healthy dose of context.
The 13 Terms That Matter When You Compare AI Hardware
You do not need an engineering degree to buy the right GPU. You need thirteen terms, sorted here in the order they will meet you in a real purchasing decision.
The model and what it needs
- Parameters (model size). The number of weights in a model, quoted in billions such as 7B or 70B. Bigger models are usually more capable but need proportionally more memory and compute. This is the number that drives almost every hardware decision.
- Inference versus training. Inference is running a finished model to get answers. Training or fine-tuning teaches it, and needs two to four times the memory for optimizer states and gradients. Most businesses only ever need inference hardware.
- Quantization (FP16, INT8, INT4). Compressing a model’s numbers so they take less memory. FP16 uses 2 bytes per parameter, INT8 halves that, INT4 halves it again. It is the single biggest lever for fitting a large model onto smaller hardware.
- Context length and the KV cache. How much text the model can hold in mind at once. Longer context needs more memory, because the model caches every token it has seen, and that cache grows as the conversation gets longer.
Inference runs the model. Training teaches it — and typically needs two to four times the memory.
Speed you can feel
- FLOPS and TFLOPS. Raw compute, floating-point operations per second, counted in trillions. It matters most for training and for FP8 inference. For everyday serving, memory usually runs out before compute does.
- Tensor Cores. Specialized units on the GPU that do the matrix math AI depends on. Newer generations add lower-precision modes such as FP8 and FP4 that roughly multiply throughput, which is why a newer card can be far faster at the same amount of memory.
- Throughput (tokens per second). The practical speed number: how many words the model produces per second, and how many users it can serve at once. This is what people actually feel, and it tracks memory bandwidth more closely than raw TFLOPS.
The hidden bottleneck
- VRAM (memory capacity). The GPU’s onboard memory, in gigabytes. It decides whether your model fits at all. Too little VRAM and the model will not load, no matter how fast the chip is. This is the number people underestimate most often.
- Memory bandwidth. How fast the GPU reads its own memory, in terabytes per second. For inference this is the real speed limit, because the model’s weights are read from memory on every token. An H200 beats an H100 mainly on bandwidth, not compute.
- Memory type (HBM versus GDDR). HBM, used on data-center cards like the H100 and H200, is far faster and pricier than the GDDR on consumer and workstation cards. It is much of the difference between a $2,000 card and a $30,000 one, and it is mostly about memory, not the processor.
Scaling and cost
- Interconnect (NVLink versus PCIe). How multiple GPUs talk to each other. NVLink, on data-center cards, is far faster than the PCIe slot consumer cards use. It matters the moment a model is too big for one card and has to be split across several.
- TDP (power draw). How many watts the card pulls, which sets your electricity and cooling bill and the power delivery your site needs. A single H100 draws around 700 watts and an RTX 5090 around 575, and it runs whenever the model does.
- Total cost of ownership. The full bill, not the sticker: the cards, the server around them, power, cooling, and hosting or colocation over the hardware’s life. Cheap cards with high running costs often lose to efficient ones across a few years.
Every one of these terms appears as a column or a label in our GPU comparison table, so you can see them attached to real cards rather than abstract definitions.
How Much GPU Do You Actually Need?
Model size sets the floor. A commonly published approximation: at full FP16 precision, a model needs roughly 2 GB of VRAM per billion parameters. Quantization shrinks that footprint — INT8 needs about half, INT4 about a quarter. On top of whatever the weights need, plan roughly 15 to 20 percent extra for caches and activations, and more if you want long context windows.
| Model size | Full precision (FP16) | Quantized (INT4) | Example GPU that fits | Typical use |
|---|---|---|---|---|
| 7B to 8B | ~16 GB | ~5 GB | RTX 4090 24GB for dev, L40S 48GB for production | Typical use |
| 13B to 14B | ~28 GB | ~9 GB | L40S 48GB, or A100 40GB | Document Q and A, richer assistants |
| 70B | ~140 GB | ~40 GB | H100 80GB at FP8, or H200 141GB at FP16 | Production quality, customer facing |
| 175B and up | ~350 GB+ | ~100 GB+ | 8-GPU H100/H200 or B200 server | Frontier scale, high concurrency |
Two practical notes on this table. First, these are approximations for the weights plus normal overhead — a long context window or a large batch adds more. Second, training changes everything: teaching or fine-tuning a model typically needs two to four times the memory of running it, because gradients and optimizer states live in VRAM alongside the weights.
Speed follows a similar logic. For inference, memory bandwidth usually governs how fast tokens appear, which is why data-center cards with HBM memory feel disproportionately faster on large models than their TFLOPS advantage suggests.
The memory ladder: what common model sizes need at full precision versus quantized to INT4.
If you would rather skip the arithmetic, we grouped every card into four practical classes from entry hardware for prototyping to flagship cards for 70B-plus production serving in the workload tiers on the comparison page.
How to Tell Which GPU Is Better: A 4-Question Checklist
When two cards are on your shortlist, four questions settle almost every comparison.
- Does the model fit? Check VRAM first. Add up your model’s memory at the precision you will run, FP16 or INT4, plus 20 to 30 percent for context, and confirm the card’s memory covers it. If it does not fit, nothing else matters.
- Is it fast enough for your users? Look at throughput and memory bandwidth, not just TFLOPS. On a single scale where the RTX 3090 is 100, a card scoring 403 is roughly four times the inference speed. Match that to how many users you serve and how quickly they expect a reply.
- Will it scale if you grow? If one card cannot hold your model, or you expect to add users, check the interconnect. NVLink cards split large models efficiently; consumer cards on PCIe do not. A card that cannot scale is a false economy.
- What is the total cost to run it? Add power, cooling, and hosting to the purchase price across three years. A cheaper card that draws more watts and needs more units often costs more than an efficient one. Compare lifetime cost, not the sticker.
There is one more trick that makes comparison dramatically easier: indexing. Instead of juggling four spec sheets, put every card on one scale. Our comparison page scores each GPU across LLM inference, image generation and vision workloads, then indexes everything against the RTX 3090 as a 100-point baseline a score of 200 simply means roughly twice the aggregate throughput of that well-known card. Suddenly the question which is better has a one-glance answer.
One scale for every card: when the RTX 3090 is 100, a 403 speaks for itself.
You can see the full indexed field in the 78-GPU table or jump straight to a head-to-head duel of any two cards. The H100 versus H200 question, for example, resolves in seconds: identical compute on paper, but 141 GB of faster memory on the H200 against 80 GB on the H100.
A Free Way to Compare: Inside the GPU Benchmark Tool
Full transparency: the tool described here is ours. It is free, it works without any signup, and it exists because we kept answering the same comparison questions for customers by hand. Here is what each part does — including what it cannot do.
The 78-GPU table
Every current card in one sortable table: VRAM, the AI Inference Index with a proportional bar, theoretical FP16 TFLOPS, published training throughput where it exists, and a plain-language note on what each card is best for. Filter by VRAM class, search by name, and sort any column. A podium of the current top three sits above it for the impatient.
Its honest limitation: the GPU table deliberately shows no prices. Market prices for AI hardware move weekly, and a stale price is worse than none — pricing happens in a quote, not in a static table.
The head-to-head arena
Pick any two cards and the arena puts them side by side: VRAM, inference index, FP16 compute and training throughput as mirrored bars, plus a written verdict in one sentence of plain English. It is the fastest way to settle an internal debate between two shortlisted cards.
Its limitation: the verdict reflects the published index, not your exact software stack. A card that wins the index can still lose on your specific framework or model version — which is why the verdict names the margin instead of pretending to be a lab report.
The arena in three steps: pick two cards, compare the bars, read the verdict.
48 complete AI servers
Once one card is not enough, the unit of comparison changes: a multi-GPU server is judged by its combined memory and combined compute. The server section lists 48 complete systems — from compact workstations to 8-GPU rack machines — with total VRAM and total FP16 compute calculated on the same scale as the single cards, so the numbers stay comparable across the whole page.
Tiers, methodology and the fine print
The workload tiers translate hardware classes into plain use cases — Flagship for 70B-plus multi-tenant serving, High-End for 30B to 70B production work, Mid-Range for the 8B to 30B bracket, Entry for prototyping and learning. The methodology is published openly: only publicly available benchmark data, three real workload families, everything indexed to the RTX 3090 at 100. And the page repeats its own disclaimer, which we repeat here: reference data for orientation, not a guarantee of performance. A 16-question FAQ covers the details.
Five Mistakes First-Time Buyers Make
- Buying compute when memory is the real limit. First-timers chase TFLOPS and pick a fast card that cannot hold their model. For inference, VRAM decides what runs, so confirm the model fits before you look at speed.
- Under-sizing VRAM to save money. A 24 GB card looks fine until a 70B model or a long context will not load. Running out of memory is a hard wall, not a slowdown. Size for the model you will run in a year, not just today.
- Ignoring quantization and over-buying. Many workloads run well at INT4 or INT8, cutting memory needs by half or three-quarters with little quality loss. Buyers who skip this pay for far more hardware than they need.
- Forgetting power, cooling, and hosting. The card is one line on the bill. A single data-center GPU can pull 700 watts and run around the clock, and it needs somewhere built to power and cool it. Budget the full running cost.
- Putting consumer cards in a production data center. GeForce cards like the RTX 4090 and 5090 are strong for development, but NVIDIA’s license restricts data-center use, and they lack ECC and NVLink. For production, workstation or data-center cards such as the L40S, A100, or H100 are the compliant choice.
From Shortlist to Quote: What to Prepare and What Happens Next
At some point the spreadsheet phase ends and you talk to a supplier. The conversation is short and productive if you bring five facts:
- The model or models you plan to run, and their size in parameters. If you are not sure, describe the task, a private chatbot, document search, or image generation, and roughly how capable it needs to be.
- How many people will use it at once, and the volume you expect: a handful of internal users, or hundreds of concurrent requests. Concurrency drives how much memory and how many cards you need.
- Whether you need inference only, or training and fine-tuning. Training needs far more memory and changes the hardware entirely. Most businesses start with inference and add training later if they need it.
- Your timeline and budget. When it needs to be live, and the range you are working within. This decides whether to buy what ships today or wait, and whether to buy outright or host.
- Where it will run: your own server room, a colocation site, or fully hosted. This shapes power, cooling, and the shape of the quote.
With those answered, here is how it works with us, stated plainly: you send the question through the form on the comparison page or via WhatsApp or Telegram, and a real engineer — not a bot — replies, usually within about two hours. The 78 GPUs and 48 servers on the page are what we list publicly; we can source well beyond that, quote B2B and volume pricing, ship worldwide with customs handled, or run the hardware for you in our hosting facilities. And to position ourselves honestly: MillionMiner is a reseller and hosting provider, not a manufacturer — which is exactly why a recommendation is not tied to any single brand.
Frequently Asked Questions
Do we need in-house hardware expertise to buy an AI server?
No. If you can answer the five preparation questions above — model, users, latency, inference or training, location — a competent supplier can translate them into hardware. The vocabulary in this guide exists so you can check their reasoning, not so you have to do their job.
Should we buy GPUs or rent them in the cloud?
A widely published pattern: workloads that run at sustained high utilization pay for owned hardware surprisingly quickly, while spiky or experimental workloads favor renting. Many businesses land in the middle — owning the hardware and having it hosted, which combines predictable cost with none of the facility work. Run the numbers on your realistic utilization before deciding.
Does a quantized model lose noticeable quality?
Published evaluations consistently show FP8 as nearly indistinguishable from full precision for most tasks, and INT4 as a small, task-dependent trade-off. The sensible approach is to test your specific use case at the quantization level you plan to run — before you size the hardware for it.
What information should we prepare before requesting a quote?
The model and its size, expected concurrent users or request volume, your latency expectation, whether you need training as well as inference, and where the hardware should live. Five answers — that is the whole homework.
Should we wait for the next GPU generation?
There is always a next generation. Buy for a two-to-three-year workload horizon: if hardware available today serves your current and near-term models with headroom, waiting mostly costs you the months of value you did not capture. New generations tend to add memory and efficiency rather than change what is possible.
The Bottom Line
Model size sets your memory floor. Memory bandwidth sets your real-world speed. Benchmarks — read as reference data, indexed to one baseline settle the comparison between any two candidates. Everything else is arithmetic on your own workload.
When you are ready to put names to numbers, the GPU and AI server comparison shows the whole field on one scale, and the AI hardware catalog shows what is in stock right now.
