Specifications Compared
| Spec | GB300 | RTX-5090 |
|---|---|---|
| TDP | 1400W | 575W |
| VRAM | 288 GB | 32 GB |
| Memory Type | HBM3e | GDDR7 |
| Architecture | Blackwell Ultra | Blackwell |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | PCIe 5.0 |
| FP8 Performance | 4,500 TFLOPS | 838 TFLOPS |
| FP16 Performance | 2,250 TFLOPS | 419 TFLOPS |
| FP32 Performance | 90 TFLOPS | 105 TFLOPS |
| FP64 Performance | 45 TFLOPS | 1.6 TFLOPS |
| INT8 Performance | 4,500 TOPS | 838 TOPS |
| Memory Bandwidth | 12,000 GB/s | 1,792 GB/s |
Performance Analysis
FP16 performance defines AI training efficiency: GB300 achieves 2250 TFLOPS, over 5x the RTX 5090's 419 TFLOPS. This enables GB300 to process larger datasets faster in deep learning pipelines. FP8 at 4500 TFLOPS on GB300 supports high-throughput inference, far exceeding 838 TFLOPS on RTX 5090.
FP32 compute is competitive, with RTX 5090 at 105 TFLOPS slightly ahead of GB300's 90 TFLOPS, benefiting graphics or simulations. Memory bandwidth profoundly impacts batch sizes: GB300's 12000 GB/s versus 1792 GB/s allows 6.7x larger batches, cutting training iterations for LLMs and reducing time to convergence.
VRAM disparity, 288 GB on GB300 against 32 GB, prevents RTX 5090 from loading models over 70B parameters without fragmentation. GB300's NVLink scales multi-GPU clusters, while PCIe 5.0 limits RTX 5090 to single-node tasks.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 5090
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA GeForce RTX 5090 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Chubbuck, Idaho | $0.57/GPU/hr | Available | ||
 Vast.ai | NVIDIA GeForce RTX 5090 32GB VRAM | 32GB | 384 vCPU 94GB RAM 642GB Storage | Czechia | $0.83/GPU/hr | Available | ||
Vast.ai | NVIDIA GeForce RTX 5090 32GB VRAM | 32GB | 8 vCPU 30GB RAM 489GB Storage | South Korea | $0.87/GPU/hr | Available | ||
Vast.ai | NVIDIA GeForce RTX 5090 32GB VRAM | 32GB | 16 vCPU 30GB RAM 583GB Storage | South Korea | $0.87/GPU/hr | Available | ||
Vast.ai | NVIDIA GeForce RTX 5090 32GB VRAM | 32GB | 16 vCPU 30GB RAM 395GB Storage | South Korea | $0.87/GPU/hr | Available |
When to Choose the GB300
The GB300 excels in enterprise AI training for models requiring over 100B parameters, leveraging 288 GB HBM3e VRAM to avoid offloading. Its 12000 GB/s bandwidth supports massive batch sizes, accelerating convergence in distributed setups via NVLink and NVSwitch. Datacenters prioritize this for production-scale LLM development.
When to Choose the RTX 5090
Individuals and small teams choose RTX 5090 for cost-effective cloud access at $0.16 per hour average $0.71 per hour across 19 offers. Its 575W TDP fits personal rigs or edge inference, handling Stable Diffusion or fine-tuning up to 70B models with 32 GB GDDR7. PCIe 5.0 enables quick prototyping without datacenter overhead.
Use Cases
GB300's 288 GB VRAM and 2250 TFLOPS FP16 handle massive models without sharding. Its 12000 GB/s bandwidth supports large batches for faster training.
GB300's 4500 TFLOPS FP8 and 288 GB VRAM enable high-throughput serving of 100B+ parameter models. RTX 5090 limits to smaller models with 32 GB.
GB300 accommodates full model loading with 288 GB VRAM during fine-tuning. 12000 GB/s bandwidth reduces epochs compared to RTX 5090's constraints.
RTX 5090's 105 TFLOPS FP32 and $0.16 per hour pricing suit image generation workflows. 32 GB GDDR7 handles typical diffusion models efficiently.
GB300's 90 TFLOPS FP32 and NVLink scaling excel in simulations needing high memory. 288 GB VRAM supports complex datasets beyond RTX 5090's 32 GB.
Frequently Asked Questions
What is the VRAM difference between GB300 and RTX 5090?▾
GB300 provides 288 GB HBM3e VRAM, enabling large models. RTX 5090 offers 32 GB GDDR7, suitable for smaller workloads. This 9x gap affects model capacity.
How do their FP16 performances compare?▾
GB300 delivers 2250 TFLOPS in FP16, 5.4x higher than RTX 5090's 419 TFLOPS. This boosts AI training speed on GB300. Inference benefits similarly.
What are the power requirements?▾
GB300 has a 1400W TDP for datacenter use. RTX 5090 consumes 575W, fitting consumer setups. Lower TDP aids RTX 5090 in portable clouds.
Is there pricing for these GPUs on gpuperhour.com?▾
RTX 5090 starts at $0.16 per hour, averaging $0.71 per hour across 19 offers. GB300 has no live offers currently. RTX 5090 provides immediate cloud access.
Which has higher memory bandwidth?▾
GB300 achieves 12000 GB/s, 6.7x more than RTX 5090's 1792 GB/s. This supports larger batches on GB300. Bandwidth directly impacts training efficiency.
What interconnects do they use?▾
GB300 employs NVSwitch and NVLink for multi-GPU scaling. RTX 5090 uses PCIe 5.0 for single-node tasks. GB300 enables massive clusters.
Which is cheaper to rent, the GB300 or the RTX 5090?▾
Cloud rental prices for both the GB300 and RTX 5090 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 RTX 5090?▾
The GB300 has 288 GB of HBM3e memory. The RTX 5090 has 32 GB of GDDR7 memory.
Can I find GB300 and RTX 5090 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 RTX 5090?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the RTX 5090 uses Blackwell (2025). The GB300 delivers 5.4x the FP16 throughput and 6.7x the memory bandwidth of the RTX 5090.