GPU Comparison

Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.

Workstation Verdict

The RTX PRO 5000 Blackwell has more VRAM (48GB vs 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 500% higher (1344 GB/s vs 224 GB/s), translating directly to faster inference throughput.

Maximum Capacity Reached. Remove a model to add another. (2/2)

VS
NVIDIA
RTX 2000 Ada Generation
Price
£650
VRAM
16 GB GDDR6
Mem. Speed
224 GB/s
FP32 Compute
12 TFLOPS
Key Specs Advantage

Comparable or lower specs

Price
Awaiting Data
VRAM
48 GB GDDR7
Mem. Speed
1344 GB/s
FP32 Compute
72.2 TFLOPS
Key Specs Advantage
+502% FP32 (TFLOPS) (72.2 TFLOPS vs 12 TFLOPS)
+500% Bandwidth (1,344 GB/s vs 224 GB/s)
+400% CUDA Cores (14,080 vs 2,816)

RTX 2000 Ada Generation vs RTX PRO 5000 Blackwell: In-Depth Breakdown

VRAM: RTX 2000 Ada Generation vs RTX PRO 5000 Blackwell

The RTX PRO 5000 Blackwell carries 48GB of VRAM versus 16GB on the RTX 2000 Ada Generation. VRAM capacity is the primary constraint for running AI models without quantization — a 70B-parameter model in FP16 requires roughly 140GB, and even smaller models benefit from extra headroom. The 32GB advantage here means the RTX PRO 5000 Blackwell can run larger models natively and handle bigger batch sizes in production.

Inference Speed: Memory Bandwidth

Memory bandwidth determines how quickly data is fed to the compute units — it's the main bottleneck for autoregressive inference (token generation in LLMs). The RTX PRO 5000 Blackwell delivers 1344 GB/s versus 224 GB/s on the RTX 2000 Ada Generation, a 500% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX PRO 5000 Blackwell will produce tokens proportionally faster in bandwidth-bound workloads.

AI Training & Compute

For model training, scientific simulation, and rendering, FP32 throughput is the key metric. The RTX PRO 5000 Blackwell delivers 72.2 TFLOPS against 12 TFLOPS for the RTX 2000 Ada Generation — a 502% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX PRO 5000 Blackwell.

Which should you buy: RTX 2000 Ada Generation or RTX PRO 5000 Blackwell?

The RTX PRO 5000 Blackwell is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX 2000 Ada Generation is more economical, and sufficient if your models fit within its 16GB.

Frequently Asked Questions

Can the RTX 2000 Ada Generation or RTX PRO 5000 Blackwell run large language models?

Both can, but the RTX PRO 5000 Blackwell (48GB) handles larger models without quantization. The RTX 2000 Ada Generation (16GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the RTX 2000 Ada Generation or the RTX PRO 5000 Blackwell?

The RTX PRO 5000 Blackwell is faster for token generation — its 1344 GB/s memory bandwidth vs 224 GB/s on the RTX 2000 Ada Generation is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX PRO 5000 Blackwell has the advantage at 72.2 TFLOPS vs 12 TFLOPS, making training runs proportionally faster than on the RTX 2000 Ada Generation.

Technical Specifications Comparison

Architecture & Cores

SpecificationRTX 2000 Ada GenerationRTX PRO 5000 Blackwell
ArchitectureAda LovelaceBlackwell
CUDA Cores (CUDA Cores / CUDA Cores)2,81614,080

Memory

SpecificationRTX 2000 Ada GenerationRTX PRO 5000 Blackwell
VRAM Capacity16 GB48 GB
Memory TypeGDDR6GDDR7
Memory Bus128-bit384-bit
Bandwidth224 GB/s1,344 GB/s

Connectivity & Power

SpecificationRTX 2000 Ada GenerationRTX PRO 5000 Blackwell
InterfacePCIe 4.0 x8PCIe 5.0 x16
TDP70 W300 W
ReleasedMar 2023Mar 2025

Workstation

SpecificationRTX 2000 Ada GenerationRTX PRO 5000 Blackwell
FP32 (TFLOPS)12 TFLOPS72.2 TFLOPS
ECCYesYes
NVLinkNoNo
Form factorlow-profiledual-slot