Specifications Compared
| Spec | B300 | RTX-4080 |
|---|---|---|
| TDP | 1200W | 320W |
| VRAM | 288 GB | 16 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 | 48.7 TFLOPS |
| FP32 Performance | 90 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | 780 TOPS |
| Memory Bandwidth | 12,000 GB/s | 717 GB/s |
Performance Analysis
The B300 SXM6 excels in FP16 at 2250 TFLOPS, surpassing the RTX 4080 SUPER's 48.7 TFLOPS by a factor of 46: this gap accelerates AI training and inference where half-precision dominates. Its FP32 reaches 90 TFLOPS over the RTX 4080 SUPER's 48.7 TFLOPS, while FP8 hits 4500 TFLOPS for quantized inference tasks. Balanced FP16 and FP32 on RTX 4080 SUPER suit graphics or general compute better than B300's AI skew. VRAM disparity defines limits: 288 GB on B300 enables trillion-parameter models and large batch sizes, versus 16 GB on RTX 4080 SUPER restricting to smaller datasets. Bandwidth of 12000 GB/s on B300 sustains data flow for high-throughput training, compared to 717 GB/s on RTX 4080 SUPER which bottlenecks large batches. TDP stands at 1200W for B300 versus 320W for RTX 4080 SUPER, impacting power efficiency in clusters.
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 | |||
 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 4080 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the B300 SXM6
Select the B300 SXM6 for large-scale LLM training or inference demanding over 16 GB VRAM: its 288 GB HBM3e handles models exceeding RTX 4080 SUPER capacity. NVSwitch and NVLink interconnects optimize multi-GPU scaling at 2250 TFLOPS FP16. High bandwidth of 12000 GB/s supports massive datasets in enterprise environments.
When to Choose the RTX 4080 SUPER
Opt for RTX 4080 SUPER in cost-sensitive scenarios like prototyping or small-model inference: pricing from $0.17 per hour fits budgets under B300's $2.45 minimum. Its 320W TDP and PCIe form factor integrate easily into standard servers. Balanced 48.7 TFLOPS FP16 and FP32 perform well for Stable Diffusion or fine-tuning under 16 GB VRAM.
Use Cases
B300 SXM6's 288 GB VRAM and 2250 TFLOPS FP16 enable training trillion-parameter models, far beyond RTX 4080 SUPER's 16 GB limit.
4500 TFLOPS FP8 and 12000 GB/s bandwidth on B300 SXM6 accelerate high-volume inference; RTX 4080 SUPER suits only small batches.
B300 SXM6's 90 TFLOPS FP32 and vast VRAM handle large fine-tuning datasets; 16 GB on RTX 4080 SUPER constrains model sizes.
RTX 4080 SUPER's 48.7 TFLOPS FP16 and $0.17 per hour pricing optimize image generation; B300 overkill for typical workflows.
RTX 4080 SUPER suffices for FP32 tasks at 48.7 TFLOPS and low cost; B300's 90 TFLOPS FP32 scales for parallel simulations.
Frequently Asked Questions
Which GPU has more VRAM, B300 SXM6 or RTX 4080 SUPER?▾
The B300 SXM6 provides 288 GB HBM3e VRAM, compared to 16 GB GDDR6X on RTX 4080 SUPER. This allows B300 to load massive AI models. Memory bandwidth reaches 12000 GB/s on B300 versus 717 GB/s on RTX 4080 SUPER.
How do FP16 performances compare between B300 SXM6 and RTX 4080 SUPER?▾
B300 SXM6 achieves 2250 TFLOPS FP16, exceeding RTX 4080 SUPER's 48.7 TFLOPS by 46 times. This boosts AI training speed on B300. FP8 on B300 hits 4500 TFLOPS for inference.
What are the cloud pricing differences for these GPUs?▾
B300 SXM6 starts at $2.45 per hour, averaging $6.44 across seven offers. RTX 4080 SUPER begins at $0.17 per hour, averaging $0.32 across three offers. Pricing reflects enterprise versus consumer positioning.
Which GPU is better for power efficiency?▾
RTX 4080 SUPER consumes 320W TDP, far below B300 SXM6's 1200W. This makes RTX suitable for dense, low-power deployments. B300 requires advanced cooling for high performance.
Can RTX 4080 SUPER handle large model training?▾
RTX 4080 SUPER's 16 GB VRAM limits it to models under that threshold, unlike B300 SXM6's 288 GB. Batch sizes suffer from 717 GB/s bandwidth. Use RTX for smaller-scale tasks.
What architectures power these GPUs?▾
B300 SXM6 uses 2025 Blackwell Ultra architecture with NVLink interconnect. RTX 4080 SUPER employs 2022 Ada Lovelace with PCIe form factor. The generational gap drives B300's spec superiority.
Which is cheaper to rent, the B300 or the RTX 4080?▾
Cloud rental prices for both the B300 and RTX 4080 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 4080?▾
The B300 has 288 GB of HBM3e memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find B300 and RTX 4080 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 4080?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX 4080 uses Ada Lovelace (2022). The B300 delivers 46.2x the FP16 throughput and 16.7x the memory bandwidth of the RTX 4080.