GPU Comparison

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

Workstation Verdict

The RTX PRO 6000 Blackwell has more VRAM (96GB vs 48GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 33% higher (1792 GB/s vs 1344 GB/s), translating directly to faster inference throughput.

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

VS
Price
Awaiting Data
VRAM
48 GB GDDR7
Mem. Speed
1344 GB/s
FP32 Compute
72.2 TFLOPS
Key Specs Advantage

Comparable or lower specs

NVIDIA
RTX PRO 6000 Blackwell
Price
£11,330
VRAM
96 GB GDDR7
Mem. Speed
1792 GB/s
FP32 Compute
125 TFLOPS
Key Specs Advantage
+73% FP32 (TFLOPS) (125 TFLOPS vs 72.2 TFLOPS)
+71% CUDA Cores (24,064 vs 14,080)
+33% Bandwidth (1,792 GB/s vs 1,344 GB/s)

RTX PRO 5000 Blackwell vs RTX PRO 6000 Blackwell: In-Depth Breakdown

VRAM: RTX PRO 5000 Blackwell vs RTX PRO 6000 Blackwell

The RTX PRO 6000 Blackwell carries 96GB of VRAM versus 48GB on the RTX PRO 5000 Blackwell. 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 48GB advantage here means the RTX PRO 6000 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 6000 Blackwell delivers 1792 GB/s versus 1344 GB/s on the RTX PRO 5000 Blackwell, a 33% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX PRO 6000 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 6000 Blackwell delivers 125 TFLOPS against 72.2 TFLOPS for the RTX PRO 5000 Blackwell — a 73% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX PRO 6000 Blackwell.

Which should you buy: RTX PRO 5000 Blackwell or RTX PRO 6000 Blackwell?

The RTX PRO 6000 Blackwell is the stronger choice for large-model workloads where VRAM is the bottleneck. The RTX PRO 5000 Blackwell is more economical, and sufficient if your models fit within its 48GB.

Frequently Asked Questions

Can the RTX PRO 5000 Blackwell or RTX PRO 6000 Blackwell run large language models?

Both can, but the RTX PRO 6000 Blackwell (96GB) handles larger models without quantization. The RTX PRO 5000 Blackwell (48GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the RTX PRO 5000 Blackwell or the RTX PRO 6000 Blackwell?

The RTX PRO 6000 Blackwell is faster for token generation — its 1792 GB/s memory bandwidth vs 1344 GB/s on the RTX PRO 5000 Blackwell is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX PRO 6000 Blackwell has the advantage at 125 TFLOPS vs 72.2 TFLOPS, making training runs proportionally faster than on the RTX PRO 5000 Blackwell.

Technical Specifications Comparison

Architecture & Cores

SpecificationRTX PRO 5000 BlackwellRTX PRO 6000 Blackwell
ArchitectureBlackwellBlackwell
CUDA Cores (CUDA Cores / CUDA Cores)14,08024,064

Memory

SpecificationRTX PRO 5000 BlackwellRTX PRO 6000 Blackwell
VRAM Capacity48 GB96 GB
Memory TypeGDDR7GDDR7
Memory Bus384-bit512-bit
Bandwidth1,344 GB/s1,792 GB/s

Connectivity & Power

SpecificationRTX PRO 5000 BlackwellRTX PRO 6000 Blackwell
InterfacePCIe 5.0 x16PCIe 5.0 x16
TDP300 W600 W
ReleasedMar 2025Mar 2025

Workstation

SpecificationRTX PRO 5000 BlackwellRTX PRO 6000 Blackwell
FP32 (TFLOPS)72.2 TFLOPS125 TFLOPS
ECCYesYes
NVLinkNoNo
Form factordual-slotdual-slot