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GPU Comparison

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

Workstation Verdict

Its memory bandwidth is 29% higher (360 GB/s vs 280 GB/s), translating directly to faster inference throughput.

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

VS
NVIDIA
RTX 4000 Ada Generation
Price
Awaiting Data
VRAM
20 GB GDDR6✓
Mem. Speed
360 GB/s✓
FP32 Compute
26.7 TFLOPS✓
Key Specs Advantage
+39% FP32 (TFLOPS) (26.7 TFLOPS vs 19.2 TFLOPS)
+29% Bandwidth (360 GB/s vs 280 GB/s)
NVIDIA
RTX 4000 SFF Ada Generation
Price
Awaiting Data
VRAM
20 GB GDDR6✓
Mem. Speed
280 GB/s
FP32 Compute
19.2 TFLOPS
Key Specs Advantage

Comparable or lower specs

Skip to Spec Sheets ↓

RTX 4000 Ada Generation vs RTX 4000 SFF Ada Generation: In-Depth Breakdown

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 4000 Ada Generation delivers 360 GB/s versus 280 GB/s on the RTX 4000 SFF Ada Generation, a 29% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX 4000 Ada Generation 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 4000 Ada Generation delivers 26.7 TFLOPS against 19.2 TFLOPS for the RTX 4000 SFF Ada Generation — a 39% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX 4000 Ada Generation.

Which should you buy: RTX 4000 Ada Generation or RTX 4000 SFF Ada Generation?

Both cards serve similar workloads. Base your decision on whichever spec matters most: VRAM for model capacity, memory bandwidth for inference speed, and FP32 compute for training throughput.

Frequently Asked Questions

Can the RTX 4000 Ada Generation or RTX 4000 SFF Ada Generation run large language models?

Yes — with 20GB of VRAM each, both support a similar range of models. Memory bandwidth and compute throughput then differentiate their performance.

Which is faster for LLM inference, the RTX 4000 Ada Generation or the RTX 4000 SFF Ada Generation?

The RTX 4000 Ada Generation is faster for token generation — its 360 GB/s memory bandwidth vs 280 GB/s on the RTX 4000 SFF Ada Generation is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX 4000 Ada Generation has the advantage at 26.7 TFLOPS vs 19.2 TFLOPS, making training runs proportionally faster than on the RTX 4000 SFF Ada Generation.

Technical Specifications Comparison

Architecture & Cores

SpecificationRTX 4000 Ada GenerationRTX 4000 SFF Ada Generation
ArchitectureAda LovelaceAda Lovelace
CUDA Cores (CUDA Cores / CUDA Cores)6,1446,144

Memory

SpecificationRTX 4000 Ada GenerationRTX 4000 SFF Ada Generation
VRAM Capacity20 GB20 GB
Memory TypeGDDR6GDDR6
Memory Bus160-bit160-bit
Bandwidth360 GB/s✓280 GB/s

Connectivity & Power

SpecificationRTX 4000 Ada GenerationRTX 4000 SFF Ada Generation
InterfacePCIe 4.0 x16PCIe 4.0 x16
TDP130 W70 W✓

Workstation

SpecificationRTX 4000 Ada GenerationRTX 4000 SFF Ada Generation
FP32 (TFLOPS)26.7 TFLOPS✓19.2 TFLOPS
ECCYesYes
NVLinkNoNo
Form factorsingle-slotlow-profile
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