Massed Compute

The RTX PRO 4500 fits the models you actually run

Most teams building with AI today run models in the 8 billion to 32 billion parameter range. That covers chat, coding help, summarizing documents, powering an agent, and answering questions over your own data.

Those models do not need a giant GPU. They need enough memory to hold the model, enough speed to feel instant, and a price that works when the meter runs all month.

The NVIDIA RTX PRO 4500 Blackwell Server Edition is built for that middle. It has 32 GB of memory and runs on the Blackwell architecture. On the Massed Compute marketplace it starts at $0.76 an hour with no contract.

It is also the least expensive Blackwell card in our marketplace. Every other one costs more than twice as much per GPU hour.

One note on naming before we go further. There are two RTX PRO 4500 Blackwell cards. This article covers the Server Edition, which is the one you rent in a data center. The Workstation Edition carries the same 32 GB but runs at 896 GB per second instead of 800.

What 32 GB and 800 GB per second gets you

Two numbers decide how a GPU handles a language model.

The first is memory. If the model fits in GPU memory, it runs fast. If it does not fit, you usually get an out of memory error rather than a slow result. Some setups can offload to system memory instead, and that runs ten to fifty times slower. Either way, memory capacity sets what you can run at all.

The second is memory bandwidth. Once the model fits, bandwidth sets how quickly the GPU can read those weights for every token it writes. That is what makes output feel quick instead of sluggish.

The RTX PRO 4500 brings 32 GB of GDDR7 memory and 800 GB per second of bandwidth. In practice you can run a 32 billion parameter model at 4-bit and still have room for a long conversation.

Here is how the common sizes land.

Native FP4, the RTX PRO 4500 advantage explained simply

Model weights get stored at some level of precision. Most models ship at 16 bits per weight. Cutting that to 4 bits shrinks the model to a quarter of the size, and for most work the quality difference is small.

There is more than one way to store 4 bits. The common approach uses 4-bit integers, and older GPUs handle that by converting the weights back up to a higher precision before doing the math. That conversion costs time on every token.

Blackwell adds a 4-bit floating point format called NVFP4, and the fifth generation Tensor Cores run it directly with no conversion step.

Testing by Exxact, a server maker that builds systems around this card, ran Llama 3.1 8B on a single RTX PRO 4500 through vLLM. At NVFP4 it served 4,870 tokens per second across many concurrent requests, against 2,031 at BF16. That is 2.4 times the work from the same card at the same $0.76 an hour. Time to first token dropped from 25 milliseconds to 13.

This is the biggest reason to pick the RTX PRO 4500 over an older card at a similar price. The L40, L40S, and RTX 6000 Ada all cost about the same per hour, and none of them support FP4.

One thing to plan for. You need an NVFP4 checkpoint and a runtime that knows about it. Today that means vLLM or TensorRT-LLM rather than a plain 4-bit download.

RTX PRO 4500 benchmark numbers on real models

We ran Muse Glimmer 30B on this card ourselves, and published the full method and the raw results so you can check them.

Two different things got measured, because they answer different questions.

One person chatting

This uses Meta’s own 4-bit build of the model through llama.cpp. The same file ran on both cards, so it is a fair comparison.

Decode speed is the number you feel while text streams back, and the two cards are nearly tied. The RTX PRO 4500 costs less per hour, so the same work comes out about 16 percent lower per token.

One place the L40S wins. It reads your prompt faster, at 2,161 tokens per second against 1,431. If you send very long prompts and expect short answers, that gap is worth knowing. For chat and agent work, where the model writes more than it reads, the RTX PRO 4500 is the better deal.

Many people at once

Serving an app is a different job. Here the card runs 32 requests at the same time through vLLM, and what matters is total output rather than one person’s stream.

A single RTX PRO 4500 produced 945.8 output tokens per second. That works out to roughly $0.22 per million output tokens.

Worth being precise about the checkpoint. That run used a community 4-bit conversion of the model rather than one published by Meta or NVIDIA, because Meta’s official 4-bit build only comes in a format vLLM does not serve. It is labeled that way in our results.

Adding cards does not speed up one conversation

We ran the same model on 1, 2, 4, and 8 cards.

Decode held between 35 and 36 tokens per second the whole way up. More cards spread the model thinner rather than making it faster.

So use extra cards to serve more people at once, or to run something that does not fit on one card. For a model that already fits, one card per session is the pattern that pays.

Reading these numbers

The single-person figures and the many-people figures are not comparable to each other. 35 tokens per second is what one person sees. 945 tokens per second is everything the card produces across 32 conversations. Different tests, different engines, different questions.

More about the model we tested

Meta released Muse Glimmer 30B in August 2026 under an Apache 2.0 license. The language model compresses to under 20 GB at 4-bit and carries a 131,000 token context window. Meta built it for agents that run all the time, rather than for one question at a time.

Meta targets a 24 GB or 32 GB card for the whole stack, which includes the language model, its context, a perception encoder, and a drafter model. So a 32 GB card is the size this model was shaped for, which is why we picked it as the first thing to measure on this card.

How the RTX PRO 4500 compares to the L40S, A100, and H100

All prices below are current Massed Compute hourly rates for a single GPU.

Three things stand out.

It costs less than the three Ada cards and adds FP4. The L40, L40S, and RTX 6000 Ada all sit between $0.79 and $0.88. They carry more memory, which helps if your model needs it. For a model that already fits in 32 GB, that extra memory sits idle while FP4 does real work.

It is the entry point to Blackwell. The next Blackwell option is the RTX PRO 6000 at $2.19. If your model fits in 32 GB, matching the card to the job saves about 65 percent an hour.

More cards serve more people, not one person faster. Benchmarks show that adding GPUs barely changes speed for a model that already fits on one card. The pattern that works is one card per active session. Four cards serve four people at full speed for $3.04 an hour.

Where a bigger card serves you better

Being straight about the ceiling saves everyone time.

A 70 billion parameter model fits on this card only at 3-bit compression, and it runs slowly. Adding a second card helps, though published figures for that setup vary enough that we would rather show you our own before quoting one. For the 70B tier the RTX PRO 6000 Blackwell at 96 GB or an H100 is the better starting point.

The same goes for serving hundreds of people at once from one machine. At that scale, cards with high bandwidth memory are the right tool.

If your models live between 8B and 32B, the RTX PRO 4500 is the better economics. Above that, size up.

Three ways teams use the RTX PRO 4500

Serving language models. A single card covers the 8B to 32B range with room for long context, and FP4 raises how much you can serve at once. Read more about running language models on the RTX PRO 4500

Understanding video. Three decode engines pull video in while the GPU runs a vision model on the frames. NVIDIA measured up to 100 times the performance of a CPU only server for AI video understanding, using a server with eight of these cards. Read more about video understanding on the RTX PRO 4500

Vector search and RAG. Building a vector index is the first slow step for most teams working with their own documents. NVIDIA measured up to 50 times the performance of a CPU only server using cuVS, again on a server with eight cards. Read more about vector search on the RTX PRO 4500

RTX PRO 4500 configurations and pricing

On demand has no contract and no minimum. You pay for the hours you use.

The 8x configuration gives you 256 GB of total memory for $6.08 an hour. That suits a team serving several mid-size models side by side. Keep in mind it delivers capacity rather than raw speed, since one request does not get faster across more cards.

Bare metal and reserved capacity are both available. Those are priced for your workload, so contact us for a quote.

How to launch an RTX PRO 4500 instance

1. Sign in to the Massed Compute marketplace and open Deploy
2. Under Select GPU Type, pick the RTX PRO 4500 configuration you want
3. Choose a Category, then an Image or setup recipe that matches your work
4. Add an SSH key and a startup command if you want them. Both are optional.
5. Launch

The instance comes up in a few minutes with a public IP.

There is a faster way to do all of this. The Massed Compute MCP server connects an AI assistant like Claude, Cursor, or ChatGPT straight to your account. You describe the machine you want in plain language, and it checks live inventory, launches the instance, and hands back your SSH details. It can also track what you are spending and shut things down when you are finished. See what the MCP server can do and how to set it up

Common questions about the RTX PRO 4500

How much memory does the RTX PRO 4500 have?

32 GB of GDDR7, with 800 GB per second of bandwidth.

What does the RTX PRO 4500 cost per hour?

A single card is $0.76 an hour on demand with no contract. Multi card configurations scale from there.

Can it run a 70B model?

It fits at heavy compression and runs slowly. For that model size the RTX PRO 6000 Blackwell or an H100 will serve you better.

How does it compare to the L40S?

The L40S has more memory at 48 GB. The RTX PRO 4500 runs a newer architecture that supports FP4, and it costs less per hour. For models that fit in 32 GB, the RTX PRO 4500 gets more work done for less.

What is the difference between the Server Edition and the Workstation Edition?

Both carry 32 GB of GDDR7. The Server Edition runs at 800 GB per second and is the card you rent in a data center. The Workstation Edition runs at 896 GB per second and goes in a desktop.

Is there a contract?

On demand has no contract. Reserved and bare metal options are available if you want committed capacity, and those are quoted for your workload.