GPU Comparison

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

Workstation Verdict

The RTX 5000 Ada Generation has more VRAM (32GB vs 24GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 33% higher (768 GB/s vs 576 GB/s), translating directly to faster inference throughput. The RTX A5000 is $255 GBP cheaper than the RTX 5000 Ada Generation.

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

VS
NVIDIA
RTX 5000 Ada Generation
Price
£2,755
VRAM
32 GB GDDR6
Mem. Speed
576 GB/s
FP32 Compute
65.3 TFLOPS
Key Specs Advantage
+135% FP32 (TFLOPS) (65.3 TFLOPS vs 27.8 TFLOPS)
+56% CUDA Cores (12,800 vs 8,192)
NVIDIA
RTX A5000
Price
£2,500
VRAM
24 GB GDDR6
Mem. Speed
768 GB/s
FP32 Compute
27.8 TFLOPS
Key Specs Advantage
+50% Memory Bus (384-bit vs 256-bit)
+33% Bandwidth (768 GB/s vs 576 GB/s)

RTX 5000 Ada Generation vs RTX A5000: In-Depth Breakdown

VRAM: RTX 5000 Ada Generation vs RTX A5000

The RTX 5000 Ada Generation carries 32GB of VRAM versus 24GB on the RTX A5000. 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 RTX 5000 Ada Generation 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 A5000 delivers 768 GB/s versus 576 GB/s on the RTX 5000 Ada Generation, a 33% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A5000 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 5000 Ada Generation delivers 65.3 TFLOPS against 27.8 TFLOPS for the RTX A5000 — a 135% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX 5000 Ada Generation.

Price & Value

The RTX A5000 lists from $2,500 GBP, $255 GBP less than the RTX 5000 Ada Generation at $2,755 GBP. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.

Which should you buy: RTX 5000 Ada Generation or RTX A5000?

These cards suit different priorities. Choose the RTX 5000 Ada Generation if fitting larger models in VRAM is your constraint. Choose the RTX A5000 if your models already fit and you want faster inference throughput from its higher memory bandwidth.

Frequently Asked Questions

Can the RTX 5000 Ada Generation or RTX A5000 run large language models?

Both can, but the RTX 5000 Ada Generation (32GB) handles larger models without quantization. The RTX A5000 (24GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the RTX 5000 Ada Generation or the RTX A5000?

The RTX A5000 is faster for token generation — its 768 GB/s memory bandwidth vs 576 GB/s on the RTX 5000 Ada Generation is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX 5000 Ada Generation has the advantage at 65.3 TFLOPS vs 27.8 TFLOPS, making training runs proportionally faster than on the RTX A5000.

Technical Specifications Comparison

Architecture & Cores

SpecificationRTX 5000 Ada GenerationRTX A5000
ArchitectureAda LovelaceAmpere
CUDA Cores (CUDA Cores / CUDA Cores)12,8008,192

Memory

SpecificationRTX 5000 Ada GenerationRTX A5000
VRAM Capacity32 GB24 GB
Memory TypeGDDR6GDDR6
Memory Bus256-bit384-bit
Bandwidth576 GB/s768 GB/s

Connectivity & Power

SpecificationRTX 5000 Ada GenerationRTX A5000
InterfacePCIe 4.0 x16PCIe 4.0 x16
TDP250 W230 W
ReleasedNov 2022Apr 2021

Workstation

SpecificationRTX 5000 Ada GenerationRTX A5000
FP32 (TFLOPS)65.3 TFLOPS27.8 TFLOPS
ECCYesYes
NVLinkNoYes
Form factordual-slotdual-slot