Specifications Compared
| Spec | B300 | RTX-2070 |
|---|---|---|
| TDP | 1200W | 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
The B300's FP16 throughput of 2250 TFLOPS dwarfs the RTX 2070's 7.5 TFLOPS: this enables 300 times faster half-precision training for large language models and inference tasks. In FP32, B300 achieves 90 TFLOPS against 7.5 TFLOPS, providing 12 times the single-precision compute for scientific simulations or graphics rendering. These metrics translate to real-world acceleration in deep learning pipelines where mixed-precision workflows dominate.
Memory specifications define scalability limits: B300's 288 GB HBM3e supports batch sizes for models exceeding hundreds of billions of parameters, while RTX 2070's 8 GB GDDR6 restricts users to small models or low-resolution inference. Bandwidth at 12000 GB/s on B300 versus 448 GB/s on RTX 2070 allows 27 times faster data movement, reducing bottlenecks in training loops and enabling larger effective batch sizes. Power draw further differentiates them, with B300's 1200W TDP suiting rack-scale deployments and RTX 2070's 175W fitting edge or desktop use.
Interconnects highlight deployment contexts: B300 leverages NVSwitch and NVLink for multi-GPU scaling, whereas RTX 2070 uses PCIe and basic NVLink, limiting cluster efficiency.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300
| 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 | |||
 VERDA | NVIDIA B300 SXM6 262GB VRAM | 262GB | 30 vCPU 255GB RAM | Helsinki | $7.50/GPU/hr | Available | ||
VERDA | 2×NVIDIA B300 SXM6 262GB VRAM | 262GB | 60 vCPU 510GB RAM | Helsinki | $7.50/GPU/hr $15.00/hr total (2×) | Available | ||
VERDA | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 240 vCPU 2040GB RAM | Helsinki | $7.50/GPU/hr $60.00/hr total (8×) | Available | ||
 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 |
When to Choose the B300
The B300 excels in enterprise AI training and large-scale inference: its 288 GB VRAM handles models like trillion-parameter LLMs, and 2250 TFLOPS FP16 supports rapid iterations. Cloud users prioritize it for production workloads where 12000 GB/s bandwidth sustains high throughput across NVSwitch clusters, justifying $6.44 per hour average cost.
Datacenter operators select B300 for FP8 tasks at 4500 TFLOPS, ideal for optimized inference serving massive user bases.
When to Choose the RTX 2070
The RTX 2070 suits budget prototyping and lightweight inference: at $0.04 per hour average, it runs small models within 8 GB VRAM limits. Developers use it for quick Stable Diffusion generations or fine-tuning compact networks, leveraging 7.5 TFLOPS FP16 without high power demands.
Gaming or personal projects favor its 175W TDP and PCIe form factor, offering accessible entry into GPU acceleration.
Use Cases
B300's 288 GB VRAM and 2250 TFLOPS FP16 enable training of massive models with large batch sizes. RTX 2070's 8 GB limits it to tiny datasets.
B300 supports high-concurrency inference via 4500 TFLOPS FP8 and 12000 GB/s bandwidth. RTX 2070 handles only low-volume queries due to 448 GB/s constraints.
B300's 90 TFLOPS FP32 accelerates parameter-efficient fine-tuning on large models. RTX 2070 suffices for small adapters but stalls on memory-intensive tasks.
RTX 2070 generates images quickly at 7.5 TFLOPS for consumer use cases. B300 overkill unless scaling to production pipelines.
B300's 1200W TDP and NVSwitch suit HPC simulations with 90 TFLOPS FP32. RTX 2070 fits basic desktop analysis only.
Frequently Asked Questions
Which GPU has more VRAM: B300 or RTX 2070?▾
The B300 provides 288 GB HBM3e VRAM, compared to 8 GB GDDR6 on the RTX 2070. This allows B300 to load vastly larger models without swapping.
How does B300 FP16 performance compare to RTX 2070?▾
B300 achieves 2250 TFLOPS in FP16, versus 7.5 TFLOPS on RTX 2070. This results in approximately 300 times faster AI training and inference.
What is the memory bandwidth difference?▾
B300 offers 12000 GB/s bandwidth, while RTX 2070 provides 448 GB/s. The 27-fold increase on B300 supports larger batch sizes in deep learning.
Which is cheaper in the cloud?▾
RTX 2070 starts at $0.02 per hour (average $0.04), far below B300's $2.45 per hour (average $6.44). Budget tasks favor RTX 2070.
Can RTX 2070 scale like B300?▾
RTX 2070 uses PCIe and basic NVLink, limiting multi-GPU setups. B300 employs NVSwitch for efficient datacenter clustering.
What are the power requirements?▾
B300 demands 1200W TDP for high-performance compute. RTX 2070 uses 175W, suitable for low-power consumer applications.
Which is cheaper to rent, the B300 or the RTX 2070?▾
Cloud rental prices for both the B300 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 B300 have compared to the RTX 2070?▾
The B300 has 288 GB of HBM3e memory. The RTX 2070 has 8 GB of GDDR6 memory.
Can I find B300 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 B300 and the RTX 2070?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX 2070 uses Turing (2018). The B300 delivers 300.0x the FP16 throughput and 26.8x the memory bandwidth of the RTX 2070.