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GPU Comparison

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

Workstation Verdict

The Quadro RTX 8000 has more VRAM (48GB vs 8GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 50% higher (624 GB/s vs 416 GB/s), translating directly to faster inference throughput.

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

VS
NVIDIA
Quadro RTX 4000
Price
Awaiting Data
VRAM
8 GB GDDR6
Mem. Speed
416 GB/s
FP32 Compute
7.1 TFLOPS
Key Specs Advantage

Comparable or lower specs

NVIDIA
Quadro RTX 8000
Price
Awaiting Data
VRAM
48 GB GDDR6✓
Mem. Speed
624 GB/s✓
FP32 Compute
14.9 TFLOPS✓
Key Specs Advantage
+110% FP32 (TFLOPS) (14.9 TFLOPS vs 7.1 TFLOPS)
+100% CUDA Cores (4,608 vs 2,304)
+50% Bandwidth (624 GB/s vs 416 GB/s)
Skip to Spec Sheets ↓

Quadro RTX 4000 vs Quadro RTX 8000: In-Depth Breakdown

VRAM: Quadro RTX 4000 vs Quadro RTX 8000

The Quadro RTX 8000 carries 48GB of VRAM versus 8GB on the Quadro RTX 4000. 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 40GB advantage here means the Quadro RTX 8000 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 Quadro RTX 8000 delivers 624 GB/s versus 416 GB/s on the Quadro RTX 4000, a 50% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the Quadro RTX 8000 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 Quadro RTX 8000 delivers 14.9 TFLOPS against 7.1 TFLOPS for the Quadro RTX 4000 — a 110% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the Quadro RTX 8000.

Which should you buy: Quadro RTX 4000 or Quadro RTX 8000?

The Quadro RTX 8000 is the stronger choice for large-model workloads where VRAM is the bottleneck. The Quadro RTX 4000 is more economical, and sufficient if your models fit within its 8GB.

Frequently Asked Questions

Can the Quadro RTX 4000 or Quadro RTX 8000 run large language models?

Both can, but the Quadro RTX 8000 (48GB) handles larger models without quantization. The Quadro RTX 4000 (8GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the Quadro RTX 4000 or the Quadro RTX 8000?

The Quadro RTX 8000 is faster for token generation — its 624 GB/s memory bandwidth vs 416 GB/s on the Quadro RTX 4000 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The Quadro RTX 8000 has the advantage at 14.9 TFLOPS vs 7.1 TFLOPS, making training runs proportionally faster than on the Quadro RTX 4000.

Technical Specifications Comparison

Architecture & Cores

SpecificationQuadro RTX 4000Quadro RTX 8000
ArchitectureTuringTuring
CUDA Cores (CUDA Cores / CUDA Cores)2,3044,608✓

Memory

SpecificationQuadro RTX 4000Quadro RTX 8000
VRAM Capacity8 GB48 GB✓
Memory TypeGDDR6GDDR6
Memory Bus256-bit384-bit✓
Bandwidth416 GB/s624 GB/s✓

Connectivity & Power

SpecificationQuadro RTX 4000Quadro RTX 8000
InterfacePCIe 3.0 x16PCIe 3.0 x16
TDP160 W✓295 W
ReleasedOct 2018Oct 2018

Workstation

SpecificationQuadro RTX 4000Quadro RTX 8000
FP32 (TFLOPS)7.1 TFLOPS14.9 TFLOPS✓
ECCYesYes
NVLinkNoYes✓
Form factorsingle-slotdual-slot
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