GPU Comparison

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

Workstation Verdict

The Radeon PRO W7800 has more VRAM (32GB vs 24GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 17% higher (672 GB/s vs 576 GB/s), translating directly to faster inference throughput.

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

VS
Price
Awaiting Data
VRAM
32 GB GDDR6
Mem. Speed
576 GB/s
FP32 Compute
45.2 TFLOPS
Key Specs Advantage
+33% Memory Bus (256-bit vs 192-bit)
NVIDIA
RTX PRO 4000 Blackwell
Price
$2,933 CAD
VRAM
24 GB GDDR7
Mem. Speed
672 GB/s
FP32 Compute
46 TFLOPS
Key Specs Advantage
+133% CUDA Cores (8,960 vs 3,840)
+17% Bandwidth (672 GB/s vs 576 GB/s)
+2% FP32 (TFLOPS) (46 TFLOPS vs 45.2 TFLOPS)

Radeon PRO W7800 vs RTX PRO 4000 Blackwell: In-Depth Breakdown

VRAM: Radeon PRO W7800 vs RTX PRO 4000 Blackwell

The Radeon PRO W7800 carries 32GB of VRAM versus 24GB on the RTX PRO 4000 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 8GB advantage here means the Radeon PRO W7800 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 4000 Blackwell delivers 672 GB/s versus 576 GB/s on the Radeon PRO W7800, a 17% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX PRO 4000 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 4000 Blackwell delivers 46 TFLOPS against 45.2 TFLOPS for the Radeon PRO W7800 — a 2% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX PRO 4000 Blackwell.

Which should you buy: Radeon PRO W7800 or RTX PRO 4000 Blackwell?

These cards suit different priorities. Choose the Radeon PRO W7800 if fitting larger models in VRAM is your constraint. Choose the RTX PRO 4000 Blackwell if your models already fit and you want faster inference throughput from its higher memory bandwidth.

Frequently Asked Questions

Can the Radeon PRO W7800 or RTX PRO 4000 Blackwell run large language models?

Both can, but the Radeon PRO W7800 (32GB) handles larger models without quantization. The RTX PRO 4000 Blackwell (24GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the Radeon PRO W7800 or the RTX PRO 4000 Blackwell?

The RTX PRO 4000 Blackwell is faster for token generation — its 672 GB/s memory bandwidth vs 576 GB/s on the Radeon PRO W7800 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX PRO 4000 Blackwell has the advantage at 46 TFLOPS vs 45.2 TFLOPS, making training runs proportionally faster than on the Radeon PRO W7800.

Technical Specifications Comparison

Architecture & Cores

SpecificationRadeon PRO W7800RTX PRO 4000 Blackwell
ArchitectureRDNA 3Blackwell
CUDA Cores (Stream Processors / CUDA Cores)3,8408,960

Memory

SpecificationRadeon PRO W7800RTX PRO 4000 Blackwell
VRAM Capacity32 GB24 GB
Memory TypeGDDR6GDDR7
Memory Bus256-bit192-bit
Bandwidth576 GB/s672 GB/s

Connectivity & Power

SpecificationRadeon PRO W7800RTX PRO 4000 Blackwell
InterfacePCIe 4.0 x16PCIe 5.0 x16
TDP260 W140 W
ReleasedMar 2023Mar 2025

Workstation

SpecificationRadeon PRO W7800RTX PRO 4000 Blackwell
FP32 (TFLOPS)45.2 TFLOPS46 TFLOPS
ECCYesYes
NVLinkNoNo
Form factordual-slotsingle-slot