Specifications Compared
| Spec | B300 | RTX-4070 |
|---|---|---|
| TDP | 1200W | 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
The B300's FP16 throughput of 2250 TFLOPS dwarfs the RTX 4070 Ti's 29.1 TFLOPS: this enables the B300 to handle large-scale model training and inference far faster, processing tensor operations central to deep learning. For FP32 tasks, the B300 delivers 90 TFLOPS against 29.1 TFLOPS, providing superior general compute while its FP8 capability of 4500 TFLOPS suits quantized inference. Memory bandwidth defines practical limits: the B300's 12000 GB/s supports enormous batch sizes in training, fitting models exceeding 100 billion parameters into 288 GB VRAM without swapping, whereas the RTX 4070 Ti's 504 GB/s and 12 GB VRAM restrict it to small batches or models under 7 billion parameters. Power draw underscores efficiency differences: 1200W for B300 versus 200W for RTX 4070 Ti, with interconnects like NVSwitch and NVLink enabling B300 scaling in multi-GPU clusters unavailable on PCIe-based RTX 4070 Ti.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300 SXM6
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA B300 SXM6 262GB VRAM | 262GB | 0 vCPU 0GB RAM | 🌍global | $7.39/GPU/hr | |||
 Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
RTX 4070 Ti
| 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 B300 SXM6
Enterprises running large language model training select the B300: its 288 GB HBM3e VRAM accommodates models over 1 trillion parameters, and 12000 GB/s bandwidth sustains high throughput. Multi-GPU inference deployments benefit from NVLink and NVSwitch for low-latency scaling across nodes. Cloud budgets exceeding $2.45 per hour justify the B300 for production AI where speed trumps cost.
When to Choose the RTX 4070 Ti
Developers prototyping small models or running Stable Diffusion choose the RTX 4070 Ti: 12 GB GDDR6X VRAM suffices for models up to 7 billion parameters at $0.08 per hour. Gaming workloads or lightweight inference leverage its 200W TDP and PCIe form factor for easy integration. Cost-sensitive users avoid the B300's $6.44 average hourly rate for non-enterprise tasks.
Use Cases
The B300's 288 GB VRAM and 2250 TFLOPS FP16 handle massive datasets and models exceeding 100 billion parameters. RTX 4070 Ti's 12 GB limits it to tiny scales.
B300 supports high-concurrency inference with 12000 GB/s bandwidth for large batches. RTX 4070 Ti struggles beyond small models due to 504 GB/s and 12 GB VRAM.
B300's 90 TFLOPS FP32 and vast memory fit full fine-tuning of large models. RTX 4070 Ti requires heavy quantization on 12 GB.
RTX 4070 Ti excels at image generation with 29.1 TFLOPS FP16 at $0.08 per hour. B300 overkill for consumer-scale diffusion at $2.45 per hour.
B300's 90 TFLOPS FP32 and NVLink scaling accelerate simulations. RTX 4070 Ti's 29.1 TFLOPS suits only modest workloads.
Frequently Asked Questions
Which GPU has more VRAM?▾
The B300 provides 288 GB HBM3e VRAM compared to 12 GB GDDR6X on the RTX 4070 Ti. This allows B300 to load models over 20 times larger without offloading. Cloud users pay from $2.45 per hour for B300 access.
What is the performance difference in FP16?▾
B300 achieves 2250 TFLOPS FP16 versus RTX 4070 Ti's 29.1 TFLOPS, a 77-fold advantage for AI tasks. This translates to faster training epochs on large datasets. Pricing reflects this: B300 averages $6.44 per hour.
How do memory bandwidths compare?▾
B300 offers 12000 GB/s bandwidth against 504 GB/s on RTX 4070 Ti, enabling 24 times larger batch sizes. High bandwidth reduces training time significantly. RTX 4070 Ti starts at $0.08 per hour for lighter loads.
What are the power requirements?▾
B300 demands 1200W TDP in SXM form factor, while RTX 4070 Ti uses 200W in PCIe. B300 suits datacenter cooling; RTX 4070 Ti fits desktops. Hourly costs: B300 $2.45 minimum, RTX 4070 Ti $0.08.
Which is cheaper in the cloud?▾
RTX 4070 Ti pricing begins at $0.08 per hour averaging $0.22 across five offers. B300 starts at $2.45 averaging $6.44 across seven. Choice depends on workload scale.
Can RTX 4070 Ti scale like B300?▾
RTX 4070 Ti lacks NVSwitch or NVLink, relying on PCIe interconnects. B300 enables multi-GPU clusters for distributed training. This makes B300 ideal for production AI.
Which is cheaper to rent, the B300 or the RTX 4070?▾
Cloud rental prices for both the B300 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 B300 have compared to the RTX 4070?▾
The B300 has 288 GB of HBM3e memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find B300 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 B300 and the RTX 4070?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX 4070 uses Ada Lovelace (2023). The B300 delivers 77.3x the FP16 throughput and 23.8x the memory bandwidth of the RTX 4070.