RTX PRO 5500 for local AI: wait for the price, not just the VRAM
The RTX PRO 5500 is worth watching if you want more memory on one GPU. It is not a reason to put a working build on hold yet. NVIDIA’s product page lists 84 GB of GDDR7, but it also says “Coming Soon” and marks the specs as preliminary. The page we checked on September 20 does not list a price or a shipping date.
That distinction matters. The r/LocalLLaMA thread calling it “released” had 244 comments when we checked. Price and cooling kept coming up. In a separate r/LocalLLM buying discussion, people were weighing a GPU build against a Mac, a Spark, or waiting. Those are useful questions. The price guesses and speed claims in the replies are not a shopping list.
What NVIDIA has actually confirmed
These are published specifications, not measurements from our own testing. The 5500 figures remain subject to change.
| Card | GPU memory | Memory bandwidth | Published card power |
|---|---|---|---|
| RTX PRO 5500 Blackwell Workstation Edition | 84 GB GDDR7 ECC | 1398 GB/sec | Up to 600 W |
| RTX PRO 6000 Blackwell Workstation Edition | 96 GB GDDR7 ECC | 1792 GB/sec | 600 W |
The 5500 has less memory and lower published bandwidth than the 6000 Workstation Edition, but not a lower maximum power figure. Do not read the smaller model number as a promise of a cooler, quieter desktop. These are card ratings, not measured inference power or whole-computer consumption. We have no noise or tokens-per-second result to attach to them.
NVIDIA describes the 5500 as designed for rack-mounted workstations. Its spec table lists active air cooling, with a liquid-cooled option in the RXM form factor. Before ordering, get the exact board dimensions, power connector requirements, and chassis compatibility from the seller or system builder. Do not buy the surrounding parts on the strength of a product photo. Our power and cooling guide covers the questions to ask.
For context, NVIDIA lists 32 GB of GDDR7 on the RTX 5090. The appeal of the 5500 is a larger memory pool on one card, not a verified speed advantage over the 5090. You can compare the already-tracked RTX 5090 and PRO 6000 in our side-by-side view. The 5500 is not in that comparison yet.
Check your workload before deciding to wait
Start with the exact model and quantization you want, then the context length and number of simultaneous requests. “It loads” is not the same test as “it handles my work.” Ollama’s FAQ documents that parallel requests increase context memory allocation. It also explains that a model can be loaded partly in GPU memory and partly in system memory.
If you already use Ollama, run ollama ps with your model loaded. Its
Processor column distinguishes 100% GPU, 100% CPU, and a CPU/GPU split.
That is a useful first check, not a speed benchmark. Then try your actual
long prompt and simultaneous requests. Record the memory use and the time
you wait for an answer. If you are buying your first machine, ask for that
same test with the exact model, quantization, runtime, and settings before
treating a seller’s demo as evidence.
Use our model-fit tool to shortlist tracked hardware and the quantization guide to understand the file choices. The tool is a planning estimate, not a measured fit test for the 5500. Do not assume every byte of the advertised memory is available for model weights.
One larger card can also avoid a split. Ollama says it places a model on one GPU when it fits, and spreads it across available GPUs when it does not. If you are considering multiple cards instead, read the multi-GPU guide before buying them. Neither option gets a speed guarantee from adding up memory.
My buying rule is simple:
- Your current model fits and answers fast enough: keep using it. A new card needs to solve a problem you actually have.
- You need more memory on one GPU and can wait: keep the 5500 on your shortlist. Wait for a real quote, a delivery date, and a test of your workload before choosing it over the 6000.
- You need a machine now: choose among confirmed configurations and delivery dates. “Coming Soon” is not a delivery commitment.
The missing price is not a small detail. It is what will tell us whether giving up memory and bandwidth against the PRO 6000 is a sensible trade. Until then, this is a card to watch, not a bargain we can recommend.