Specifications Compared
| Spec | GB300 | RTX-4070 |
|---|---|---|
| TDP | 1400W | 200W |
| VRAM | 288 GB | 12 GB |
| Memory Type | HBM3e | GDDR6X |
| Architecture | Blackwell Ultra | Ada Lovelace |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 29.1 TFLOPS |
| FP32 Performance | 90 TFLOPS | 29.1 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | 466 TOPS |
| Memory Bandwidth | 12,000 GB/s | 504 GB/s |
Performance Analysis
Compute disparities define usability: the GB300 SXM6's 2250 TFLOPS FP16 accelerates AI training via efficient half-precision tensor operations, enabling models with trillions of parameters, while the RTX 4070 SUPER's 35.5 TFLOPS FP16 suits smaller neural networks. In FP32 for precise simulations, 90 TFLOPS on GB300 SXM6 doubles the 35.5 TFLOPS of RTX 4070 SUPER, shortening scientific computation cycles.
Memory bandwidth dictates batch processing: 12000 GB/s on GB300 SXM6 supports vast batches in LLM training, minimizing I/O bottlenecks for datasets exceeding hundreds of gigabytes, versus 504 GB/s on RTX 4070 SUPER which constrains large-model handling. The GB300's FP16-to-FP32 ratio of 25:1 optimizes AI-specific tensor cores; RTX 4070 SUPER's parity favors graphics rendering alongside compute.
Inference benefits from GB300's 4500 TFLOPS FP8, serving thousands of queries per second, far beyond consumer limits.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the GB300 SXM6
Select the GB300 SXM6 for large-scale AI training and inference: its 288 GB HBM3e VRAM accommodates full-parameter LLMs, and 12000 GB/s bandwidth sustains high batch sizes. NVLink and NVSwitch enable clusters scaling to exaFLOPS, ideal for enterprise data centers.
HPC tasks like climate modeling leverage 90 TFLOPS FP32 and 1400W efficiency in SXM racks.
When to Choose the RTX 4070 SUPER
The RTX 4070 SUPER fits consumer and prosumer setups: 12 GB GDDR6X and 504 GB/s handle Stable Diffusion generation or 4K gaming at 35.5 TFLOPS FP32. Its 220W TDP integrates into standard desktops without specialized cooling.
Small-scale fine-tuning or inference on models under 7B parameters performs efficiently on PCIe without cluster overhead.
Use Cases
GB300 SXM6's 288 GB VRAM and 2250 TFLOPS FP16 manage massive models and large batches; RTX 4070 SUPER's 12 GB VRAM restricts to tiny datasets.
4500 TFLOPS FP8 and 12000 GB/s bandwidth on GB300 SXM6 deliver high-throughput serving; RTX 4070 SUPER's 504 GB/s limits concurrent queries.
RTX 4070 SUPER's 35.5 TFLOPS FP32 suffices for models under 13B parameters; GB300 SXM6 excels for larger ones with 90 TFLOPS FP32.
RTX 4070 SUPER's 12 GB VRAM and 504 GB/s optimize image generation workflows; GB300 SXM6 overkill for single-user creative tasks.
GB300 SXM6's 90 TFLOPS FP32 and NVLink scaling accelerate simulations; RTX 4070 SUPER's 35.5 TFLOPS suits prototyping only.
Frequently Asked Questions
What is the VRAM capacity of NVIDIA GB300 SXM6 versus RTX 4070 SUPER?▾
The GB300 SXM6 provides 288 GB HBM3e VRAM for massive AI models. The RTX 4070 SUPER offers 12 GB GDDR6X, adequate for consumer applications. This 24-fold difference impacts model size handling.
How do memory bandwidths compare between GB300 SXM6 and RTX 4070 SUPER?▾
GB300 SXM6 achieves 12000 GB/s, enabling large batch training. RTX 4070 SUPER delivers 504 GB/s for gaming and small ML tasks. The gap affects data throughput by a factor of 24.
Which GPU has higher FP16 performance, GB300 SXM6 or RTX 4070 SUPER?▾
GB300 SXM6 reaches 2250 TFLOPS FP16 for AI acceleration. RTX 4070 SUPER provides 35.5 TFLOPS FP16. GB300 exceeds by over 63 times.
What are the TDP ratings for these GPUs?▾
GB300 SXM6 consumes 1400W in datacenter SXM form. RTX 4070 SUPER uses 220W for PCIe desktops. This reflects enterprise versus consumer power profiles.
Can RTX 4070 SUPER handle LLM training like GB300 SXM6?▾
RTX 4070 SUPER's 12 GB VRAM limits it to small LLMs under 7B parameters at 35.5 TFLOPS. GB300 SXM6's 288 GB and 2250 TFLOPS FP16 support trillion-parameter models. Use SUPER for prototyping only.
What interconnects do they support?▾
GB300 SXM6 uses NVSwitch and NVLink for multi-GPU clusters. RTX 4070 SUPER lacks dedicated interconnects, relying on PCIe. This enables GB300 scaling to thousands of GPUs.
Which is cheaper to rent, the GB300 or the RTX 4070?▾
Cloud rental prices for both the GB300 and RTX 4070 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 4070?▾
The GB300 has 288 GB of HBM3e memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find GB300 and RTX 4070 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 4070?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the RTX 4070 uses Ada Lovelace (2023). The GB300 delivers 77.3x the FP16 throughput and 23.8x the memory bandwidth of the RTX 4070.