GB300 vs Quadro P5000

Blackwell UltravsPascalUpdated 35 days ago

The GB300 emerges as the clear winner for prevalent AI and HPC use cases: its 2250 TFLOPS FP16 and 288 GB VRAM enable workloads infeasible on the Quadro P5000's 8.9 TFLOPS and 16 GB limits. Modern applications demand such scale, rendering the P5000 obsolete except in niche legacy scenarios.

Quadro P5000 from $0.78/hr

Specifications Compared

SpecGB300QUADRO-P5000
TDP1400W180W
VRAM288 GB16 GB
Memory TypeHBM3eGDDR5X
ArchitectureBlackwell UltraPascal
Form FactorsSXMPCIe
InterconnectNVSwitch, NVLink
FP8 Performance4,500 TFLOPS
FP16 Performance2,250 TFLOPS8.9 TFLOPS
FP32 Performance90 TFLOPS8.9 TFLOPS
FP64 Performance45 TFLOPS
INT8 Performance4,500 TOPS
Memory Bandwidth12,000 GB/s288 GB/s

Performance Analysis

Compute capabilities define the core performance gap: the GB300 delivers 2250 TFLOPS in FP16 for training large models, dwarfing the Quadro P5000's 8.9 TFLOPS. This FP16 to FP32 ratio on the GB300, 2250 TFLOPS to 90 TFLOPS, optimizes inference with FP8 at 4500 TFLOPS, enabling efficient handling of trillion-parameter LLMs. The P5000's balanced 8.9 TFLOPS across FP16 and FP32 limits it to smaller-scale tasks like basic neural networks.

Memory specifications profoundly impact real-world usage. The GB300's 288 GB HBM3e VRAM and 12000 GB/s bandwidth support massive batch sizes in training, reducing epochs from days to hours. Conversely, the P5000's 16 GB GDDR5X and 288 GB/s bandwidth constrain batch sizes, leading to frequent data swaps and slower convergence in memory-intensive inference. Power draw underscores efficiency: the GB300's 1400W TDP powers hyperscale clusters via NVSwitch and NVLink, while the P5000's 180W fits PCIe workstations without specialized cooling.

Interconnect differences affect scalability. The GB300 integrates NVSwitch for multi-GPU coherence, ideal for distributed training. The P5000 lacks such features, restricting it to single-node professional rendering.

Live Cloud Pricing

Real-time prices from 25+ providers. Updated every 60 seconds.

Quadro P5000

ProviderGPU ModelVRAMHost SpecsRegionPriceStatusAction
Paperspace
Paperspace
2×NVIDIA Quadro P5000
16GB VRAM
$0.78/GPU/hr
$1.56/hr total (2×)
Available
Paperspace
Paperspace
2×NVIDIA Quadro P5000
16GB VRAM
$0.78/GPU/hr
$1.56/hr total (2×)
Available
Paperspace
Paperspace
2×NVIDIA Quadro P5000
16GB VRAM
$0.78/GPU/hr
$1.56/hr total (2×)
Available
Paperspace
Paperspace
NVIDIA Quadro P5000
16GB VRAM
$0.78/GPU/hr
Available
Paperspace
Paperspace
NVIDIA Quadro P5000
16GB VRAM
$0.78/GPU/hr
Available

Compare real-time pricing across 25+ providers

When to Choose the GB300

The GB300 excels in datacenter environments demanding extreme scale: its 288 GB VRAM handles models exceeding 100 billion parameters during LLM training or scientific simulations. Users prioritizing FP16 throughput at 2250 TFLOPS choose it for inference on FP8-quantized workloads, where 4500 TFLOPS accelerates real-time serving. High-bandwidth interconnects like NVLink make it essential for clustered deployments in AI research labs.

When to Choose the Quadro P5000

The Quadro P5000 fits budget-conscious workstations for legacy CAD and visualization: its 16 GB VRAM and 8.9 TFLOPS FP32 suffice for moderate 3D modeling without datacenter overhead. At $0.78 per hour, it offers accessible cloud access across six providers for professionals avoiding the GB300's 1400W power demands. PCIe form factor ensures compatibility with standard hardware in small studios.

Use Cases

LLM Training
GB300

The GB300's 288 GB VRAM and 2250 TFLOPS FP16 support massive datasets and parameters unattainable on the P5000's 16 GB and 8.9 TFLOPS.

LLM Inference
GB300

FP8 performance at 4500 TFLOPS on the GB300 enables low-latency serving of large models, far beyond the P5000's 8.9 TFLOPS FP16.

Fine-tuning
GB300

12000 GB/s bandwidth on the GB300 allows large batch sizes for efficient fine-tuning, unlike the P5000's 288 GB/s constraint.

Stable Diffusion
GB300

The GB300's 90 TFLOPS FP32 and high VRAM generate high-resolution images rapidly, outperforming the P5000's equivalent 8.9 TFLOPS.

Scientific Computing
GB300

NVSwitch interconnects and 1400W TDP scalability make the GB300 ideal for simulations, contrasting the P5000's single-PCIe limitations.

Frequently Asked Questions

What is the VRAM difference between GB300 and Quadro P5000?

The GB300 provides 288 GB HBM3e VRAM, while the Quadro P5000 offers 16 GB GDDR5X. This 18-fold increase enables the GB300 to manage vastly larger models.

How does memory bandwidth compare?

GB300 achieves 12000 GB/s, compared to 288 GB/s on the Quadro P5000. Higher bandwidth reduces bottlenecks in data-heavy AI tasks.

Is the Quadro P5000 available on cloud platforms?

Yes, from $0.78 per hour across six live offers. It averages $0.78 per hour, making it economical for light workloads.

What are the FP16 performance specs?

GB300 delivers 2250 TFLOPS FP16, versus 8.9 TFLOPS on the Quadro P5000. This gap favors GB300 for modern training.

When will GB300 be available for cloud rental?

No live offers currently exist for GB300. Monitor gpuperhour.com for updates on this 2025 Blackwell Ultra GPU.

What is the power consumption difference?

GB300 requires 1400W TDP in SXM form, while Quadro P5000 uses 180W in PCIe. This suits GB300 for datacenters only.

Which is cheaper to rent, the GB300 or the Quadro P5000?

Cloud rental prices for both the GB300 and Quadro P5000 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 GB300 have compared to the Quadro P5000?

The GB300 has 288 GB of HBM3e memory. The Quadro P5000 has 16 GB of GDDR5X memory.

Can I find GB300 and Quadro P5000 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 GB300 and the Quadro P5000?

The GB300 uses the Blackwell Ultra architecture (2025) while the Quadro P5000 uses Pascal (2016). The GB300 delivers 252.8x the FP16 throughput and 41.7x the memory bandwidth of the Quadro P5000.

GB300 vs Quadro P5000: 252.8x FP16 Gap, 288GB vs 16GB | GPUPerHour