Specifications Compared
| Spec | B300 | RTX-2080 |
|---|---|---|
| TDP | 1200W | 215W |
| VRAM | 288 GB | 8-11 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 | 10.1 TFLOPS |
| FP32 Performance | 90 TFLOPS | 10.1 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 616 GB/s |
Performance Analysis
The B300's FP16 throughput of 2250 TFLOPS dwarfs the RTX 2080's 10.1 TFLOPS, accelerating deep learning training where half-precision computations dominate: training times shrink dramatically for large neural networks. Its FP32 performance of 90 TFLOPS exceeds RTX 2080's 10.1 TFLOPS, benefiting simulations requiring full single-precision accuracy. FP8 capability at 4500 TFLOPS on B300 further optimizes inference for quantized models, unavailable on the older Turing chip.
Memory specs transform workloads: B300's 288 GB HBM3e VRAM and 12000 GB/s bandwidth support enormous batch sizes and models up to hundreds of billions of parameters without swapping. RTX 2080's 8-11 GB GDDR6 at 616 GB/s limits it to small batches or models under 7 billion parameters, causing out-of-memory errors in modern AI pipelines. Interconnects amplify this: B300's NVSwitch enables terabit-scale scaling across nodes, while RTX 2080's NVLink suits dual-card setups at best.
Power and form factor impact deployment: B300's 1200W TDP demands data center cooling, ideal for sustained 24/7 runs, versus RTX 2080's efficient 215W for intermittent use.
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 |
RTX 2080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA GeForce RTX 2080 Ti 11GB VRAM | 11GB | 32 vCPU 63GB RAM 1273GB Storage | Maryland | $0.13/GPU/hr | Available |
When to Choose the B300
Opt for the B300 in large-scale AI training or inference where models exceed 100 billion parameters: its 288 GB VRAM handles them natively, unlike RTX 2080's 8-11 GB limit. Enterprise environments benefit from 12000 GB/s bandwidth for massive batches and NVSwitch for cluster efficiency, justifying $2.45 per hour starting price.
When to Choose the RTX 2080
Choose the RTX 2080 for budget-conscious prototyping or gaming: at $0.05 per hour, it delivers 10.1 TFLOPS FP32 for small ML models under 1 billion parameters or 1080p rendering. Its 215W TDP and PCIe compatibility suit desktops without data center infrastructure.
Use Cases
B300's 288 GB VRAM and 2250 TFLOPS FP16 support training models over 100B parameters without issues. RTX 2080's 8-11 GB VRAM cannot handle such scales.
4500 TFLOPS FP8 and 12000 GB/s bandwidth on B300 enable high-throughput serving of large LLMs. RTX 2080 lacks capacity for production inference.
B300's 90 TFLOPS FP32 outperforms RTX 2080's 10.1 TFLOPS for precise fine-tuning on datasets fitting 288 GB VRAM. Smaller cards suffice only for tiny models.
RTX 2080's 10.1 TFLOPS FP16 runs basic image generation adequately at low cost. B300 excels for high-resolution or batched workflows needing more VRAM.
B300's 1200W TDP sustains 90 TFLOPS FP32 for long simulations across NVSwitch clusters. RTX 2080 limits to modest desktop computations.
Frequently Asked Questions
What is the VRAM difference between B300 and RTX 2080?▾
B300 provides 288 GB HBM3e VRAM, enabling massive models. RTX 2080 offers 8-11 GB GDDR6, suitable only for smaller workloads.
How do their FP16 performances compare?▾
B300 achieves 2250 TFLOPS FP16 for rapid AI training. RTX 2080 delivers 10.1 TFLOPS, over 222 times slower.
What are the cloud rental prices?▾
B300 starts at $2.45 per hour, averaging $6.44 across seven offers. RTX 2080 begins at $0.05 per hour, averaging $0.10 across eight offers.
Can RTX 2080 handle large language models?▾
RTX 2080's 8-11 GB VRAM limits it to models under 7B parameters. B300's 288 GB supports 100B+ parameter LLMs seamlessly.
What architectures power these GPUs?▾
B300 uses Blackwell Ultra from 2025 for datacenter AI. RTX 2080 employs Turing from 2018 for gaming and entry compute.
Which has higher memory bandwidth?▾
B300's 12000 GB/s vastly exceeds RTX 2080's 616 GB/s. This allows larger batches on B300 without bottlenecks.
Which is cheaper to rent, the B300 or the RTX 2080?▾
Cloud rental prices for both the B300 and RTX 2080 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 2080?▾
The B300 has 288 GB of HBM3e memory. The RTX 2080 has 8 to 11 GB of GDDR6 memory.
Can I find B300 and RTX 2080 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 2080?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX 2080 uses Turing (2018). The B300 delivers 222.8x the FP16 throughput and 19.5x the memory bandwidth of the RTX 2080.