Specifications Compared
| Spec | B300 | QUADRO-P6000 |
|---|---|---|
| TDP | 1200W | 250W |
| VRAM | 288 GB | 24 GB |
| Memory Type | HBM3e | GDDR5X |
| Architecture | Blackwell Ultra | Pascal |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 12.6 TFLOPS |
| FP32 Performance | 90 TFLOPS | 12.6 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 432 GB/s |
Performance Analysis
B300's 2250 TFLOPS FP16 performance exceeds P6000's 12.6 TFLOPS by over 178 times, accelerating machine learning training where half-precision computations dominate. Its FP32 at 90 TFLOPS provides about 7 times the throughput of P6000's 12.6 TFLOPS, benefiting general-purpose scientific simulations. The FP8 capability of 4500 TFLOPS on B300 further optimizes large language model inference, unavailable on P6000.
Memory capacity defines workload feasibility: 288 GB HBM3e on B300 handles models exceeding 24 GB GDDR5X on P6000, enabling larger batch sizes in training without splitting across GPUs. Bandwidth at 12000 GB/s versus 432 GB/s, a 28-fold increase, reduces data transfer bottlenecks, supporting higher throughput in memory-intensive inference. These specs translate to B300 sustaining massive AI pipelines, while P6000 suits lighter, latency-sensitive tasks.
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 |
Quadro P6000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | New York | $1.10/GPU/hr | Available | ||
Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | Amsterdam | $1.10/GPU/hr | Available | ||
Paperspace | NVIDIA Quadro P6000 24GB VRAM | 24GB | 8 vCPU 30GB RAM 50GB Storage | Canada | $1.10/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro P6000 24GB VRAM | 24GB | 16 vCPU 60GB RAM 50GB Storage | New York | $1.10/GPU/hr $2.20/hr total (2×) | Available | ||
Paperspace | 2×NVIDIA Quadro P6000 24GB VRAM | 24GB | 16 vCPU 60GB RAM 50GB Storage | Amsterdam | $1.10/GPU/hr $2.20/hr total (2×) | Available |
When to Choose the B300 SXM6
NVIDIA B300 excels in large-scale AI deployments. Its 288 GB VRAM accommodates full-parameter training of models like 1-trillion-parameter LLMs, impossible on 24 GB P6000. Deploy it for inference at 4500 TFLOPS FP8 or training at 2250 TFLOPS FP16 in cloud environments starting at $2.45 per hour.
When to Choose the Quadro P6000
NVIDIA Quadro P6000 fits budget-conscious legacy applications. At $1.10 per hour with 250W TDP, it powers professional visualization or CAD without high power infrastructure. Choose it for PCIe-compatible workstations running FP32 tasks at 12.6 TFLOPS where 24 GB VRAM suffices.
Use Cases
B300's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 support full-model training without sharding. P6000's 24 GB cannot handle large LLMs.
B300 achieves 4500 TFLOPS FP8 for high-throughput serving of massive models. P6000 lacks FP8 and sufficient 12.6 TFLOPS FP16.
288 GB VRAM on B300 fits parameter-efficient fine-tuning of billion-parameter models at 90 TFLOPS FP32. P6000's 24 GB limits scale.
B300's 12000 GB/s bandwidth accelerates image generation batches with 2250 TFLOPS FP16. P6000 struggles with memory at 432 GB/s.
B300's 90 TFLOPS FP32 outperforms P6000's 12.6 TFLOPS for simulations. Vast VRAM aids complex datasets.
Frequently Asked Questions
What is the VRAM difference between B300 and Quadro P6000?▾
B300 provides 288 GB HBM3e VRAM, compared to 24 GB GDDR5X on Quadro P6000. This 12-fold increase allows B300 to load much larger models or datasets.
How do FP16 performances compare?▾
B300 delivers 2250 TFLOPS FP16, over 178 times higher than Quadro P6000's 12.6 TFLOPS. This gap favors B300 heavily in AI training.
What are the cloud pricing details?▾
B300 starts at $2.45 per hour averaging $6.44 across 7 offers. Quadro P6000 is $1.10 per hour across 6 offers.
Which has higher memory bandwidth?▾
B300 offers 12000 GB/s, 28 times the 432 GB/s of Quadro P6000. Higher bandwidth reduces bottlenecks in data-heavy tasks.
What are the TDP ratings?▾
B300 requires 1200W TDP in SXM form, versus 250W for Quadro P6000 in PCIe. P6000 suits lower-power setups.
When was each GPU released?▾
B300 uses 2025 Blackwell Ultra architecture. Quadro P6000 relies on 2016 Pascal architecture.
Which is cheaper to rent, the B300 or the Quadro P6000?▾
Cloud rental prices for both the B300 and Quadro P6000 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 P6000?▾
The B300 has 288 GB of HBM3e memory. The Quadro P6000 has 24 GB of GDDR5X memory.
Can I find B300 and Quadro P6000 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 P6000?▾
The B300 uses the Blackwell Ultra architecture (2025) while the Quadro P6000 uses Pascal (2016). The B300 delivers 178.6x the FP16 throughput and 27.8x the memory bandwidth of the Quadro P6000.