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
The B300's FP16 throughput of 2250 TFLOPS enables rapid matrix multiplications essential for deep learning training, dwarfing the Quadro P6000's 12.6 TFLOPS and allowing 178 times faster processing of half-precision workloads. For FP32 tasks common in simulations, the B300 delivers 90 TFLOPS against 12.6 TFLOPS, a sevenfold improvement that accelerates general compute without precision loss. The FP16 to FP32 delta on B300 favors mixed-precision training, reducing memory usage while maintaining accuracy, unlike the P6000's balanced but limited rates.
Memory capacity defines real-world viability: 288 GB HBM3e on B300 supports massive batch sizes for training models with billions of parameters, whereas 24 GB GDDR5X on P6000 restricts batches to small scales, often requiring gradient accumulation. Bandwidth amplifies this: 12000 GB/s on B300 sustains high data throughput for inference pipelines, minimizing stalls, compared to 432 GB/s on P6000 which bottlenecks large datasets. In inference, B300's 4500 TFLOPS FP8 performance optimizes low-latency serving of quantized models.
Power efficiency shifts dramatically with B300's 1200W TDP demanding robust cooling and infrastructure, yet yielding superior perf-per-watt in AI domains over P6000's 250W for lighter loads. Interconnects like NVSwitch and NVLink on B300 enable multi-GPU scaling unavailable on PCIe-bound P6000.
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 | |||
 VERDA | NVIDIA B300 SXM6 262GB VRAM | 262GB | 30 vCPU 255GB RAM | Helsinki | $7.50/GPU/hr | Available | ||
VERDA | 2×NVIDIA B300 SXM6 262GB VRAM | 262GB | 60 vCPU 510GB RAM | Helsinki | $7.50/GPU/hr $15.00/hr total (2×) | Available | ||
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
The B300 excels in large-scale AI training and inference where 288 GB HBM3e VRAM accommodates models exceeding 100 billion parameters without offloading. Its 2250 TFLOPS FP16 and 4500 TFLOPS FP8 performance suit hyperscale deployments, such as LLM fine-tuning on datasets over 1 TB, leveraging 12000 GB/s bandwidth for efficient scaling across NVLink clusters.
Cloud users prioritizing throughput over cost select B300 at $2.45 per hour for production workloads demanding SXM form factors and 90 TFLOPS FP32 for hybrid scientific computing.
When to Choose the Quadro P6000
The Quadro P6000 fits budget-conscious visualization tasks like CAD rendering or light simulations, where 24 GB GDDR5X handles datasets under 20 GB at 432 GB/s bandwidth. Its 250W TDP and PCIe compatibility suit edge deployments or legacy software without NVLink needs, available at a low $1.10 per hour.
Professionals migrating older workflows choose P6000 for cost savings on 12.6 TFLOPS FP32 tasks, avoiding the B300's 1200W power overhead.
Use Cases
B300's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 support training models with hundreds of billions of parameters at large batch sizes. P6000's 24 GB limits it to toy models.
B300's 4500 TFLOPS FP8 and 12000 GB/s bandwidth enable high-throughput quantized serving. P6000's 12.6 TFLOPS FP16 cannot handle production-scale requests.
288 GB VRAM on B300 fits full model fine-tuning without sharding, with 90 TFLOPS FP32 for stable gradients. P6000 requires excessive checkpointing.
B300 processes high-resolution generations rapidly via 2250 TFLOPS FP16, supporting large diffusion models. P6000 struggles with 24 GB VRAM limits.
B300's 90 TFLOPS FP32 and NVLink scaling accelerate simulations on massive grids. P6000's 12.6 TFLOPS suits only small-scale computations.
Frequently Asked Questions
What is the VRAM difference between B300 and Quadro P6000?▾
The B300 provides 288 GB of HBM3e VRAM, twelve times more than the Quadro P6000's 24 GB GDDR5X. This enables B300 to handle vastly larger models and datasets. Bandwidth follows suit at 12000 GB/s versus 432 GB/s.
How do B300 and P6000 compare in FP16 performance?▾
B300 achieves 2250 TFLOPS in FP16, over 178 times the P6000's 12.6 TFLOPS. This gap transforms AI training speed. FP8 on B300 adds 4500 TFLOPS for inference.
Which GPU is cheaper in the cloud?▾
Quadro P6000 offers start at $1.10 per hour across 6 providers, undercutting B300's $2.45 per hour minimum and $6.44 average across 7 offers. P6000 suits low-budget tasks.
What are the power requirements for these GPUs?▾
B300 demands 1200W TDP in SXM form factor with NVLink, requiring data center infrastructure. P6000 uses 250W in PCIe, ideal for standard servers.
Is B300 better for AI training than P6000?▾
Yes, B300's 288 GB VRAM, 2250 TFLOPS FP16, and 12000 GB/s bandwidth make it superior for training large LLMs. P6000's specs limit it to basic prototypes.
Can Quadro P6000 handle modern ML inference?▾
Quadro P6000's 12.6 TFLOPS FP16 and 24 GB VRAM restrict it to small models at low throughput. B300's 4500 TFLOPS FP8 excels in production inference.
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.