NVIDIA RTX PRO 5500 Blackwell 84GB Professional GPU
| Brand | NVIDIA |
| Model | RTX PRO 5500 84GB |
Product Overview
The NVIDIA RTX PRO 5500 Blackwell Workstation Edition, released in September 2026, is built on the Blackwell architecture (GB202 GPU) with 84GB of GDDR7 memory with ECC and 1,398 GB/s memory bandwidth at a 600W board power limit. Its design intent is not maximum bandwidth but maximum memory per watt of budget: by trading roughly 22% of bandwidth against capacity, it delivers 2.625× the memory of a GeForce RTX 5090 — a deliberate "capacity over bandwidth" choice for AI inference, long-context agent workflows and professional workstations.
Key Specifications
| GPU | NVIDIA Blackwell (GB202) |
|---|---|
| CUDA cores | 21,760 (per PNY provisional specifications) |
| Memory | 84GB GDDR7 with ECC |
| Memory bandwidth | 1,398 GB/s |
| Board power | 600W max |
| Interface | PCIe Gen5 x16, 4× DisplayPort 2.1b |
| MIG support | Up to 2 instances (~42GB each) |
| NVDEC | 3× 6th-generation decoders |
| Cooling | Active air cooling / RXM-spec liquid cooling for rack deployment |
| Power connector | 12VHPWR |
Positioning: RTX 5090 vs RTX PRO 5500 vs RTX PRO 6000
| GeForce RTX 5090 | 32GB GDDR7 | 1,792 GB/s | Consumer, maximum bandwidth |
|---|---|---|---|
| RTX PRO 5500 | 84GB GDDR7 ECC | 1,398 GB/s | Large-memory workstation, AI inference |
| RTX PRO 6000 | 96GB GDDR7 ECC+MIG | 1,792 GB/s | Flagship professional workstation |
Key difference: the RTX PRO 5500 carries 2.625× the memory of an RTX 5090 and only 12GB less than the RTX PRO 6000, at roughly 22% lower bandwidth. It is a clear capacity-versus-bandwidth trade: when memory becomes the primary bottleneck of AI inference, 84GB is a more practical capacity point than 72GB and cheaper than 96GB.
Why 84GB Matters for AI Inference
- Model weights: a 70B-parameter model needs about 35GB at INT4 quantization.
- KV cache: a 128K-token context adds another 8–16GB on top of the weights.
- Agent workflows: every tool call extends the context; running multiple conversations in parallel pushes memory demand up close to exponentially.
A 32GB RTX 5090 hits its ceiling quickly in these scenarios, while 84GB fits a larger model, a longer context and more parallel tasks at the same time. NVIDIA lists Agentic AI first in the RTX PRO 5500 product brief — the target user is a team that needs to run large-model inference locally or on workstations without paying for every flagship feature of the RTX PRO 6000.
Professional Features
- ECC memory: protects long-running inference, financial analytics, medical imaging and scientific computing from silent bit flips.
- MIG partitioning: up to 2 isolated instances of ~42GB for multi-tenant or batch workloads.
- Display / media: 4× DisplayPort 2.1b and 3× NVDEC for professional visualization and video pipelines.
Availability & Support
We supply the RTX PRO 5500 Blackwell 84GB in both new-retail and tested pulled conditions, from our Shenzhen stock. Every card is verified on a target platform before shipment. Bulk pricing, workstation configuration advice and cluster build consulting are available on request.
Frequently Asked Questions
Is the RTX PRO 5500 84GB compatible with my existing workstation or server?
Send us your host model and PSU rating — the card needs a 600W power budget, a 12VHPWR connector and a PCIe Gen5 (Gen4 backward-compatible) x16 slot. We confirm chassis clearance and cooling before quoting.
Do you offer new units, pulled units, or both?
Both. New-retail cards come with standard manufacturer warranty; pulled units are inspected, burned-in and shipped in anti-static packaging with a 3-12 month in-house warranty depending on grade.
Can the 84GB memory be used to run several models at once?
Yes. With MIG enabled, the card splits into 2 isolated instances of about 42GB each, letting two mid-size models serve concurrently; without MIG the full 84GB is available to a single process.
