RTX PRO 2000 Blackwell 16GB

Specs, explained

Used for Edge AI, professional graphics and inference

1. 0116 GB of GDDR7 memory

   VRAM

   16 GB

   Memory type

   GDDR7

   Memory bandwidth

   0.288 TB/s

   GDDR is graphics memory mounted on the board around the GPU rather than stacked on the package. It is widely used on graphics, rendering and cost-efficient inference cards.

   VRAM is the working space on the card. Model weights, activations and datasets have to fit in it, or be split across several GPUs. Rule of thumb for the weights alone at 16-bit precision (about 2 GB per billion parameters): up to roughly 8 billion parameters, before activations and cache. This is arithmetic on the recorded VRAM, not a benchmark.

2. 02Blackwell architecture

   Architecture

   Blackwell

   Generation

   RTX PRO Blackwell

   Launch year

   2025

   Lifecycle status

   Current

   Blackwell added fifth-generation Tensor Cores and support for lower-precision formats down to FP4. (General context about the architecture, not a statement about this specific card.)

   Launched in 2025. Kovara's records list it as a current-generation part.

3. 03Form factor and power

   NVLink

   Not supported

   MIG

   Not supported

   Form factor

   PCIe low-profile dual-slot

   TDP

   70 W

   Kovara's records show no NVLink support, so multi-GPU jobs on this part rely on PCIe or the network between servers to move data.

   PCIe cards slot into standard server and workstation expansion slots, which suits a wide range of systems.

   TDP is the rated thermal design power: the planning figure for power delivery and cooling in a server.

4. 04What it is used for

   Primary use (recorded)

   Edge AI / professional graphics / inference

   These are the uses Kovara has recorded for this part. The notes explain the workload in general terms; they are not performance claims.

   • Edge AIRunning models close to where data is produced, in compact, power-limited systems.
   • professional graphicsInteractive graphics and professional design or engineering applications.
   • inferenceRunning a trained model to answer requests. Memory capacity, latency and cost per request are the usual concerns.

Figures are Kovara master data, last verified 03 Sept 2026. Explanations describe the technology in general; they are not benchmarks and say nothing about price or availability.