GPU Comparison

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

Workstation Verdict

The Radeon PRO W7900 has more VRAM (48GB vs 16GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 200% higher (864 GB/s vs 288 GB/s), translating directly to faster inference throughput. The Radeon PRO W7700 is $2,996 USD cheaper than the Radeon PRO W7900.

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

VS
AMD
Radeon PRO W7700
Price
$999 USD
VRAM
16 GB GDDR6
Mem. Speed
288 GB/s
FP32 Compute
34.6 TFLOPS
Key Specs Advantage

Comparable or lower specs

Price
$3,995 USD
VRAM
48 GB GDDR6
Mem. Speed
864 GB/s
FP32 Compute
61.3 TFLOPS
Key Specs Advantage
+200% Bandwidth (864 GB/s vs 288 GB/s)
+200% Memory Bus (384-bit vs 128-bit)
+118% Stream Processors (6,144 vs 2,816)

Radeon PRO W7700 vs Radeon PRO W7900: In-Depth Breakdown

VRAM: Radeon PRO W7700 vs Radeon PRO W7900

The Radeon PRO W7900 carries 48GB of VRAM versus 16GB on the Radeon PRO W7700. 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 Radeon PRO W7900 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 Radeon PRO W7900 delivers 864 GB/s versus 288 GB/s on the Radeon PRO W7700, a 200% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Radeon PRO W7900 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 Radeon PRO W7900 delivers 61.3 TFLOPS against 34.6 TFLOPS for the Radeon PRO W7700 — a 77% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Radeon PRO W7900.

Price & Value

The Radeon PRO W7700 lists from $999 USD, $2,996 USD less than the Radeon PRO W7900 at $3,995 USD. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.

Which should you buy: Radeon PRO W7700 or Radeon PRO W7900?

Choose the Radeon PRO W7900 for maximum capacity — it leads on VRAM, bandwidth, and compute, making it the better fit for large models and training jobs. The Radeon PRO W7700 is the more budget-friendly option ($2,996 USD less) — a solid choice if your models fit within its 16GB and inference volume is moderate.

Frequently Asked Questions

Can the Radeon PRO W7700 or Radeon PRO W7900 run large language models?

Both can, but the Radeon PRO W7900 (48GB) handles larger models without quantization. The Radeon PRO W7700 (16GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the Radeon PRO W7700 or the Radeon PRO W7900?

The Radeon PRO W7900 is faster for token generation — its 864 GB/s memory bandwidth vs 288 GB/s on the Radeon PRO W7700 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The Radeon PRO W7900 has the advantage at 61.3 TFLOPS vs 34.6 TFLOPS, making training runs proportionally faster than on the Radeon PRO W7700.

Technical Specifications Comparison

Architecture & Cores

SpecificationRadeon PRO W7700Radeon PRO W7900
ArchitectureRDNA 3RDNA 3
CUDA Cores (Stream Processors / Stream Processors)2,8166,144

Memory

SpecificationRadeon PRO W7700Radeon PRO W7900
VRAM Capacity16 GB48 GB
Memory TypeGDDR6GDDR6
Memory Bus128-bit384-bit
Bandwidth288 GB/s864 GB/s

Connectivity & Power

SpecificationRadeon PRO W7700Radeon PRO W7900
InterfacePCIe 4.0 x16PCIe 4.0 x16
TDP190 W295 W
ReleasedApr 2023Nov 2022

Workstation

SpecificationRadeon PRO W7700Radeon PRO W7900
FP32 (TFLOPS)34.6 TFLOPS61.3 TFLOPS
ECCYesYes
NVLinkNoNo
Form factordual-slotdual-slot