Specifications Compared
| Spec | B300 | QUADRO-RTX-8000 |
|---|---|---|
| TDP | 1200W | 260W |
| VRAM | 288 GB | 48 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 | 16.3 TFLOPS |
| FP32 Performance | 90 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 672 GB/s |
Performance Analysis
FP16 performance defines AI acceleration potential: B300 delivers 2250 TFLOPS compared to 16.3 TFLOPS on Quadro RTX 8000, enabling model training epochs in minutes rather than hours for large neural networks. The FP32 rating of 90 TFLOPS on B300 exceeds Quadro RTX 8000's 16.3 TFLOPS, benefiting simulation and rendering workloads that rely on single-precision compute.
Memory specifications transform practical usability: 288 GB HBM3e on B300 supports full loading of models exceeding 100 billion parameters, while 48 GB GDDR6 on Quadro RTX 8000 limits to smaller datasets requiring model parallelism. Bandwidth disparity, 12000 GB/s versus 672 GB/s, allows B300 to sustain larger batch sizes in inference, minimizing data transfer bottlenecks and improving throughput by factors of 18.
Power demands reflect capability: B300's 1200W TDP suits data centers, contrasting Quadro RTX 8000's 260W for edge deployments.
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 | |||
 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
Select the B300 for large-scale AI development: 288 GB HBM3e VRAM accommodates trillion-parameter models without sharding, and 2250 TFLOPS FP16 accelerates training cycles. Cloud pricing from $2.45 per hour across seven providers enables scalable deployments via SXM form factor with NVSwitch interconnect.
High-bandwidth tasks favor B300: 12000 GB/s supports inference at massive batch sizes, ideal for enterprise serving.
When to Choose the Quadro RTX 8000
Opt for Quadro RTX 8000 in power-sensitive on-premises environments: 260W TDP integrates into standard PCIe workstations without extensive cooling. 48 GB GDDR6 suffices for professional visualization or moderate ML fine-tuning where NVLink multi-GPU scaling applies.
Legacy software compatibility directs choice to Quadro RTX 8000: Turing architecture from 2018 ensures stability for CAD and simulation pipelines avoiding cloud dependency.
Use Cases
B300's 288 GB HBM3e VRAM loads massive datasets without partitioning, and 2250 TFLOPS FP16 completes training epochs 138 times faster than Quadro RTX 8000's 16.3 TFLOPS.
12000 GB/s bandwidth on B300 enables large batch sizes for low-latency serving, far surpassing Quadro RTX 8000's 672 GB/s. FP8 at 4500 TFLOPS optimizes quantized inference efficiency.
90 TFLOPS FP32 on B300 handles parameter-efficient tuning of large models, with 288 GB VRAM avoiding offloading unlike Quadro RTX 8000's 48 GB limit.
B300 excels at high-resolution generations via 2250 TFLOPS FP16, but Quadro RTX 8000's 16.3 TFLOPS suffices for standard workflows in 48 GB VRAM-constrained setups.
Quadro RTX 8000's 260W TDP and PCIe form factor fit low-power HPC nodes, where 16.3 TFLOPS FP32 matches many simulation needs without B300's 1200W overhead.
Frequently Asked Questions
What is the VRAM difference between B300 and Quadro RTX 8000?▾
B300 offers 288 GB HBM3e VRAM, six times more than Quadro RTX 8000's 48 GB GDDR6. This enables B300 to process larger models without splitting across GPUs. Quadro RTX 8000 suits smaller datasets in workstations.
How does FP16 performance compare?▾
B300 achieves 2250 TFLOPS FP16, over 138 times higher than Quadro RTX 8000's 16.3 TFLOPS. Such disparity accelerates deep learning training significantly. Inference workloads benefit proportionally.
What are the power requirements?▾
B300 demands 1200W TDP in SXM form factor for data centers. Quadro RTX 8000 uses 260W in PCIe, ideal for desktops. Choose based on infrastructure cooling capacity.
Is cloud pricing available for these GPUs?▾
B300 lists from $2.45 per hour average $6.44 across seven offers. Quadro RTX 8000 has no live cloud offers. On-premises purchase applies for the latter.
Which has higher memory bandwidth?▾
B300 provides 12000 GB/s, 18 times Quadro RTX 8000's 672 GB/s. Higher bandwidth reduces bottlenecks in large-batch training. This impacts AI throughput directly.
What architectures do they use?▾
B300 employs Blackwell Ultra from 2025 with FP8 support at 4500 TFLOPS. Quadro RTX 8000 uses Turing from 2018. Newer design yields vast compute gains.
Which is cheaper to rent, the B300 or the Quadro RTX 8000?▾
Cloud rental prices for both the B300 and Quadro RTX 8000 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 Quadro RTX 8000?▾
The B300 has 288 GB of HBM3e memory. The Quadro RTX 8000 has 48 GB of GDDR6 memory.
Can I find B300 and Quadro RTX 8000 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 Quadro RTX 8000?▾
The B300 uses the Blackwell Ultra architecture (2025) while the Quadro RTX 8000 uses Turing (2018). The B300 delivers 138.0x the FP16 throughput and 17.9x the memory bandwidth of the Quadro RTX 8000.