Specifications Compared
| Spec | B300 | QUADRO-RTX-5000 |
|---|---|---|
| TDP | 1200W | 230W |
| VRAM | 288 GB | 16 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 | 11.2 TFLOPS |
| FP32 Performance | 90 TFLOPS | 11.2 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 performance reaches 2250 TFLOPS, enabling training of large language models with billions of parameters in hours, whereas the Quadro RTX 5000's 11.2 TFLOPS restricts it to modest datasets over days. FP32 throughput follows suit at 90 TFLOPS for B300 versus 11.2 TFLOPS for Quadro, impacting simulation and rendering workloads. Inference benefits from B300's FP8 at 4500 TFLOPS for high-volume deployments. Memory capacity creates a chasm: 288 GB HBM3e on B300 handles enormous models without swapping, while 16 GB GDDR6 on Quadro demands quantization or smaller batches. Bandwidth disparity of 12000 GB/s versus 448 GB/s allows B300 to process batch sizes 25 times larger without latency spikes, crucial for production inference.
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 |
Quadro RTX 5000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.82/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.82/GPU/hr $1.64/hr total (2×) | Available |
When to Choose the B300 SXM6
Select the B300 for large-scale LLM training or inference requiring over 288 GB VRAM and 2250 TFLOPS FP16, such as fine-tuning models with trillions of parameters. Datacenter environments with NVSwitch and NVLink interconnects favor it for multi-GPU scaling at 1200W TDP. High-throughput tasks like FP8 inference at 4500 TFLOPS justify $2.45 per hour starting pricing.
When to Choose the Quadro RTX 5000
Opt for Quadro RTX 5000 in budget-constrained workstations needing 16 GB VRAM for CAD or light simulations at 230W TDP and $0.82 per hour. PCIe form factor suits single-node setups with NVLink for modest parallelism. Legacy Turing-optimized software runs efficiently without overkill performance.
Use Cases
B300's 288 GB VRAM and 2250 TFLOPS FP16 enable training massive models without memory limits. Quadro's 16 GB VRAM cannot handle large datasets.
4500 TFLOPS FP8 and 12000 GB/s bandwidth on B300 support high-volume serving with large batches. Quadro's 11.2 TFLOPS FP16 limits throughput.
90 TFLOPS FP32 and vast VRAM allow efficient fine-tuning of billion-parameter models on B300. Quadro struggles with 11.2 TFLOPS and 16 GB constraints.
B300 accelerates image generation via superior FP16 at 2250 TFLOPS for high-resolution batches. Quadro suffices for basic use but bottlenecks at scale.
Quadro RTX 5000's 11.2 TFLOPS FP32 and low 230W TDP fit modest simulations cost-effectively at $0.82 per hour. B300 overpowers small-scale needs.
Frequently Asked Questions
What is the VRAM difference between B300 and Quadro RTX 5000?▾
B300 provides 288 GB HBM3e VRAM, 18 times more than Quadro RTX 5000's 16 GB GDDR6. This enables B300 to load massive AI models fully into memory. Quadro suits smaller datasets only.
How do FP16 performances compare?▾
B300 achieves 2250 TFLOPS FP16, over 200 times the Quadro RTX 5000's 11.2 TFLOPS. B300 excels in AI training speed. Quadro limits to entry-level tasks.
Which has higher memory bandwidth?▾
B300 offers 12000 GB/s, nearly 27 times Quadro RTX 5000's 448 GB/s. Larger batches process faster on B300 without bottlenecks. Quadro faces delays in data-heavy workloads.
What are the cloud rental prices?▾
B300 starts at $2.45 per hour with $6.44 average across 7 offers. Quadro RTX 5000 is $0.82 per hour across 2 offers. Cost reflects performance gulf.
Is B300 more power-hungry?▾
B300's TDP is 1200W, over five times Quadro RTX 5000's 230W. Datacenter cooling handles B300 efficiently. Quadro fits low-power workstations.
Can Quadro RTX 5000 run modern AI models?▾
Quadro RTX 5000's 16 GB VRAM restricts it to quantized small models at 11.2 TFLOPS. B300's 288 GB supports full-scale LLMs. Upgrade for demanding inference.
Which is cheaper to rent, the B300 or the Quadro RTX 5000?▾
Cloud rental prices for both the B300 and Quadro RTX 5000 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 5000?▾
The B300 has 288 GB of HBM3e memory. The Quadro RTX 5000 has 16 GB of GDDR6 memory.
Can I find B300 and Quadro RTX 5000 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 5000?▾
The B300 uses the Blackwell Ultra architecture (2025) while the Quadro RTX 5000 uses Turing (2018). The B300 delivers 200.9x the FP16 throughput and 26.8x the memory bandwidth of the Quadro RTX 5000.