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's FP16 throughput of 2250 TFLOPS vastly outpaces the RTX 4080's 48.7 TFLOPS, enabling faster AI training where half-precision dominates. Its FP32 at 90 TFLOPS exceeds the RTX 4080's 48.7 TFLOPS, supporting robust general-purpose computing, while FP8 at 4500 TFLOPS accelerates inference on quantized models. The RTX 4080 maintains parity between FP16 and FP32 at 48.7 TFLOPS each, suiting graphics rendering or balanced workloads. Memory specs define real-world limits: the B300's 288 GB HBM3e handles models with billions of parameters at large batch sizes, whereas the RTX 4080's 16 GB GDDR6X restricts it to smaller datasets. Bandwidth disparity is stark at 12000 GB/s versus 717 GB/s, reducing bottlenecks in data-heavy training and allowing the B300 to process tensors 16 times faster. Power draw reflects this: 1200W TDP for the B300 demands robust cooling, against 320W for the RTX 4080.
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 4080
| 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
Enterprises training large language models select the B300 for its 288 GB VRAM, which accommodates full model loading without sharding. High-bandwidth 12000 GB/s memory supports massive batch sizes in distributed setups via NVSwitch and NVLink interconnects. Inference at scale benefits from 4500 TFLOPS FP8 performance across clusters.
When to Choose the RTX 4080
Developers prototyping small AI models or running Stable Diffusion choose the RTX 4080 due to its low entry price of $0.11 per hour. The 16 GB VRAM suffices for models under 7 billion parameters, and 320W TDP fits standard cloud instances without high power costs. Gaming or graphics tasks leverage its balanced 48.7 TFLOPS FP16 and FP32.
Use Cases
The B300's 288 GB HBM3e VRAM fits massive models without partitioning, and 2250 TFLOPS FP16 accelerates convergence. RTX 4080's 16 GB limits scale.
4500 TFLOPS FP8 on B300 handles high-throughput quantized inference with 12000 GB/s bandwidth for large batches. RTX 4080 suits only small deployments.
RTX 4080's 16 GB VRAM works for models under 13B parameters at $0.11 per hour. B300 excels for larger ones needing 288 GB.
RTX 4080's 48.7 TFLOPS FP16 generates images quickly within 16 GB VRAM limits. B300 overkill for single-user creative tasks.
B300's 90 TFLOPS FP32 and 12000 GB/s bandwidth speed simulations with large datasets. RTX 4080 adequate only for modest computations.
Frequently Asked Questions
How much VRAM does the NVIDIA B300 have compared to RTX 4080?▾
The B300 offers 288 GB HBM3e VRAM, enabling huge models. The RTX 4080 provides 16 GB GDDR6X, suitable for smaller workloads. This 18-fold difference impacts batch sizes in training.
What is the memory bandwidth difference between B300 and RTX 4080?▾
B300 delivers 12000 GB/s, far exceeding RTX 4080's 717 GB/s. Higher bandwidth reduces data transfer delays in AI pipelines. It supports 16 times faster throughput.
Which GPU has higher FP16 performance: B300 or RTX 4080?▾
B300 achieves 2250 TFLOPS FP16, versus RTX 4080's 48.7 TFLOPS. This gap accelerates deep learning training by over 46 times. Inference benefits similarly.
What are the cloud pricing ranges for these GPUs?▾
B300 SXM6 starts at $2.45 per hour, averaging $6.44 across 7 offers. RTX 4080 begins at $0.11 per hour, averaging $0.26 over 5 offers. Budget tasks favor RTX 4080.
What is the TDP of B300 versus RTX 4080?▾
B300 requires 1200W TDP for peak performance. RTX 4080 uses 320W, easier for consumer setups. Power needs scale with compute demands.
Can RTX 4080 match B300 in AI training?▾
No, RTX 4080's 16 GB VRAM and 48.7 TFLOPS FP16 cannot handle large-scale training like B300's 288 GB and 2250 TFLOPS. Use RTX 4080 for prototyping only.
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.