Specifications Compared
| Spec | GB300 | RTX-2070 |
|---|---|---|
| TDP | 1400W | 175W |
| VRAM | 288 GB | 8 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 | 7.5 TFLOPS |
| FP32 Performance | 90 TFLOPS | 7.5 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 448 GB/s |
Performance Analysis
Raw compute reveals stark contrasts suited to different eras: the GB300 SXM6's FP16 at 2250 TFLOPS vastly outpaces its FP32 at 90 TFLOPS, a 25:1 ratio ideal for low-precision AI training and inference where tensor cores accelerate mixed-precision workflows. The RTX 2070 SUPER's FP16 near 18 TFLOPS and FP32 at 9 TFLOPS show a tighter 2:1 ratio, limiting it to smaller-scale tasks without modern tensor scaling. This delta means GB300 handles trillion-parameter models efficiently, while RTX 2070 SUPER struggles beyond modest fine-tuning.
Memory bandwidth defines practical limits: GB300 SXM6's 12000 GB/s supports massive batch sizes in training, enabling 10x larger batches than the RTX 2070 SUPER's 448 GB/s without OOM errors. Higher bandwidth reduces data starvation in deep learning pipelines, cutting training times dramatically for GB300. Power draw underscores deployment: 1400W TDP suits rack-scale clusters, versus 215W for desktop efficiency, impacting total cost of ownership in edge versus cloud scenarios.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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When to Choose the GB300 SXM6
Opt for the GB300 SXM6 in large-scale AI deployments: its 288 GB HBM3e VRAM fits entire LLMs for training, and 12000 GB/s bandwidth handles enormous batches. Datacenter environments with NVSwitch thrive on 2250 TFLOPS FP16 for rapid iteration on models exceeding 100B parameters.
When to Choose the RTX 2070 SUPER
Choose the RTX 2070 SUPER for consumer or edge applications: 8 GB GDDR6 and 448 GB/s suffice for gaming or lightweight inference at 215W TDP. Budget workstations benefit from PCIe compatibility and NVLink for multi-GPU setups without datacenter infrastructure.
Use Cases
GB300 SXM6's 288 GB VRAM and 2250 TFLOPS FP16 support massive models and large batches. RTX 2070 SUPER's 8 GB VRAM causes out-of-memory errors on large LLMs.
4500 TFLOPS FP8 on GB300 SXM6 accelerates high-throughput serving. RTX 2070 SUPER's 448 GB/s bandwidth limits concurrent requests.
Small datasets fit RTX 2070 SUPER's 8 GB VRAM for quick experiments. GB300 SXM6 excels for parameter-efficient methods on larger models.
RTX 2070 SUPER's 9 TFLOPS FP32 handles image generation at 8 GB VRAM efficiently for desktops. GB300 SXM6 overkill for single-user creative tasks.
GB300 SXM6's 12000 GB/s bandwidth and NVSwitch speed simulations with huge datasets. RTX 2070 SUPER suits modest HPC on 448 GB/s.
Frequently Asked Questions
What is the VRAM difference between GB300 SXM6 and RTX 2070 SUPER?▾
GB300 SXM6 provides 288 GB HBM3e versus 8 GB GDDR6 on RTX 2070 SUPER, a 36-fold increase. This enables loading massive AI models without swapping. Bandwidth follows suit at 12000 GB/s versus 448 GB/s.
How do FP16 performances compare?▾
GB300 SXM6 delivers 2250 TFLOPS FP16, dwarfing RTX 2070 SUPER's approximately 18 TFLOPS. The gap accelerates AI training by orders of magnitude. FP32 shows 90 TFLOPS versus 9 TFLOPS.
What are the power requirements?▾
GB300 SXM6 demands 1400W TDP for datacenter cooling, while RTX 2070 SUPER uses 215W for standard desktops. This affects deployment scalability. PCIe form factor aids RTX versatility.
Which has better memory bandwidth?▾
GB300 SXM6 offers 12000 GB/s, 27 times the RTX 2070 SUPER's 448 GB/s. Larger batches result in faster training epochs. HBM3e versus GDDR6 drives this advantage.
Are interconnects different?▾
GB300 SXM6 uses NVSwitch and NVLink for cluster scaling, beyond RTX 2070 SUPER's NVLink SLI. Multi-GPU AI benefits from GB300. PCIe limits RTX to smaller setups.
When was each architecture released?▾
Blackwell Ultra arrived in 2025 for GB300 SXM6; Turing powered RTX 2070 SUPER in 2018. Seven-year gap explains spec leaps. AI focus shifted dramatically.
Which is cheaper to rent, the GB300 or the RTX 2070?▾
Cloud rental prices for both the GB300 and RTX 2070 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 RTX 2070?▾
The GB300 has 288 GB of HBM3e memory. The RTX 2070 has 8 GB of GDDR6 memory.
Can I find GB300 and RTX 2070 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 RTX 2070?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the RTX 2070 uses Turing (2018). The GB300 delivers 300.0x the FP16 throughput and 26.8x the memory bandwidth of the RTX 2070.