Specifications Compared
| Spec | GB300 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 1400W | 260W |
| VRAM | 288 GB | 48 GB |
| Memory Type | HBM3e | GDDR6 |
| Architecture | Blackwell Ultra | Turing |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | NVLink |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 90 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 672 GB/s |
Performance Analysis
The GB300's FP16 performance of 2250 TFLOPS vastly outpaces the Quadro RTX 8000's 16.3 TFLOPS, accelerating AI training and inference where half-precision computations dominate, reducing training times from days to hours for large models. Its FP32 throughput of 90 TFLOPS exceeds the Quadro's 16.3 TFLOPS, but the relative FP16 boost signals optimization for modern deep learning over traditional single-precision tasks.
Memory bandwidth at 12000 GB/s on the GB300 supports batch sizes orders of magnitude larger than the 672 GB/s limit of the Quadro RTX 8000, minimizing data loading bottlenecks in model training and enabling inference on models exceeding 48 GB VRAM. The 288 GB HBM3e capacity handles full-parameter fine-tuning of billion-scale LLMs, impossible on the Quadro's 48 GB.
Power draw reveals trade-offs: the GB300's 1400W TDP suits datacenter cooling, while the Quadro's 260W fits standard workstations, influencing deployment in power-constrained environments.
Live Cloud Pricing
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When to Choose the GB300 SXM6
The GB300 stands out for large-scale AI training and inference, where its 2250 TFLOPS FP16 and 288 GB VRAM manage models like trillion-parameter LLMs. Datacenter operators benefit from 12000 GB/s bandwidth and NVSwitch for multi-GPU clusters.
High FP8 performance at 4500 TFLOPS makes it ideal for efficient inference deployments requiring massive throughput.
When to Choose the Quadro RTX 8000
The Quadro RTX 8000 suits professional workstations for CAD, rendering, and legacy scientific simulations, leveraging its 260W TDP and PCIe form factor for easy integration. Its 48 GB GDDR6 VRAM handles moderate datasets without datacenter infrastructure.
Users with Turing-optimized software or power-limited setups prefer its balanced 16.3 TFLOPS across FP16 and FP32.
Use Cases
The GB300's 2250 TFLOPS FP16 and 288 GB HBM3e VRAM enable training of massive LLMs with large batch sizes, far beyond the Quadro RTX 8000's 16.3 TFLOPS and 48 GB GDDR6.
With 4500 TFLOPS FP8 and 12000 GB/s bandwidth, the GB300 delivers high-throughput inference for production-scale LLMs, outperforming the Quadro RTX 8000's limited 16.3 TFLOPS FP16.
GB300's 288 GB VRAM supports full-parameter fine-tuning of large models, while 90 TFLOPS FP32 exceeds the Quadro RTX 8000's capacity constrained by 48 GB VRAM.
The GB300 accelerates diffusion model generation via 2250 TFLOPS FP16, handling high-resolution batches unavailable on the Quadro RTX 8000's 672 GB/s bandwidth.
The Quadro RTX 8000's 260W TDP and PCIe form factor fit workstation-based simulations with 16.3 TFLOPS FP32, avoiding the GB300's 1400W datacenter requirements.
Frequently Asked Questions
What is the VRAM capacity of the NVIDIA GB300 versus Quadro RTX 8000?▾
The GB300 features 288 GB HBM3e VRAM, compared to 48 GB GDDR6 on the Quadro RTX 8000. This sixfold difference allows the GB300 to load much larger models. Bandwidth follows suit at 12000 GB/s versus 672 GB/s.
How do FP16 performance levels compare between GB300 and Quadro RTX 8000?▾
GB300 achieves 2250 TFLOPS in FP16, towering over the Quadro RTX 8000's 16.3 TFLOPS. This gap accelerates AI workloads significantly. FP32 stands at 90 TFLOPS for GB300 against 16.3 TFLOPS.
Which GPU has higher power consumption?▾
The GB300 draws 1400W TDP, far exceeding the Quadro RTX 8000's 260W. This reflects datacenter versus workstation design. Cooling needs differ accordingly.
Can the Quadro RTX 8000 handle large LLM training?▾
No, its 48 GB VRAM and 16.3 TFLOPS FP16 limit it to small models. The GB300's 288 GB and 2250 TFLOPS FP16 are required for scale. Batch sizes suffer on Quadro due to 672 GB/s bandwidth.
What form factors do these GPUs use?▾
GB300 employs SXM for datacenter racks with NVSwitch support. Quadro RTX 8000 uses PCIe for desktops. Both offer NVLink interconnects.
Is the GB300 suitable for workstations?▾
No, its 1400W TDP and SXM form factor demand datacenter infrastructure. Quadro RTX 8000's 260W and PCIe fit workstations perfectly. Choose based on deployment scale.
Which is cheaper to rent, the GB300 or the Quadro RTX 8000?▾
Cloud rental prices for both the GB300 and Quadro RTX 8000 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the GB300 have compared to the Quadro RTX 8000?▾
The GB300 has 288 GB of HBM3e memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find GB300 and Quadro RTX 8000 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the GB300 and the Quadro RTX 8000?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the Quadro RTX 8000 uses Turing (2018). The GB300 delivers 138.0x the FP16 throughput and 17.9x the memory bandwidth of the Quadro RTX 8000.