Specifications Compared
| Spec | GB300 | RTX-A4000 |
|---|---|---|
| TDP | 1400W | 140W |
| VRAM | 288 GB | 16 GB |
| Memory Type | HBM3e | GDDR6 |
| Architecture | Blackwell Ultra | Ampere |
| Form Factors | SXM | PCIe |
| Interconnect | NVSwitch, NVLink | |
| FP8 Performance | 4,500 TFLOPS | |
| FP16 Performance | 2,250 TFLOPS | 19.2 TFLOPS |
| FP32 Performance | 90 TFLOPS | 19.2 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 448 GB/s |
Performance Analysis
The compute disparity defines these GPUs: GB300 SXM6 achieves 2250 TFLOPS in FP16 and 4500 TFLOPS in FP8, compared to A4500's 19.2 TFLOPS across FP16 and FP32. This gap accelerates deep learning training and inference on GB300 SXM6, where lower-precision formats like FP8 and FP16 dominate, enabling models infeasible on A4500. The GB300 SXM6 FP32 rate of 90 TFLOPS still exceeds A4500, but its low-precision emphasis suits modern AI pipelines over general graphics.
Memory specs amplify real-world impacts: 288 GB HBM3e VRAM on GB300 SXM6 supports enormous models and batch sizes, while 16 GB GDDR6 on A4500 limits to smaller datasets. Bandwidth at 12000 GB/s versus 448 GB/s prevents bottlenecks in data-heavy tasks, allowing GB300 SXM6 to process larger batches 27 times faster in memory-constrained scenarios. A4500's balanced FP16/FP32 suits mixed workloads, but GB300 SXM6 excels in scaled AI training where VRAM and throughput matter most.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX A4500
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Tallinn, Harjumaa | $0.08/GPU/hr | Available | ||
 Vast.ai | 8×NVIDIA RTX A4000 16GB VRAM | 16GB | 80 vCPU 201GB RAM 1698GB Storage | United Kingdom | $0.15/GPU/hr $1.17/hr total (8×) | Available | ||
 Hyperstack | 4×NVIDIA RTX A4000 16GB VRAM | 16GB | 16 vCPU 86GB RAM 500GB Storage | Norway | $0.15/GPU/hr $0.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA RTX A4000 16GB VRAM | 16GB | 8 vCPU 43GB RAM 200GB Storage | Norway | $0.15/GPU/hr $0.30/hr total (2×) | Available | ||
Hyperstack | NVIDIA RTX A4000 16GB VRAM | 16GB | 4 vCPU 21GB RAM 100GB Storage | Norway | $0.15/GPU/hr | Available |
When to Choose the GB300 SXM6
Opt for NVIDIA GB300 SXM6 in large-scale AI training or inference demanding over 16 GB VRAM, such as multi-billion parameter LLMs. Its 288 GB HBM3e and 12000 GB/s bandwidth handle massive datasets without splitting, paired with 2250 TFLOPS FP16 for rapid iterations. Datacenter environments with NVSwitch and NVLink thrive here, justifying 1400W TDP for hyperscale clusters.
When to Choose the RTX A4500
Select NVIDIA RTX A4500 for cost-sensitive, readily available cloud tasks like visualization or small-model fine-tuning. At $0.10 per hour average $0.19, its 140W TDP and PCIe form factor fit edge deployments or laptops. 16 GB GDDR6 suffices for Stable Diffusion or FP32-heavy scientific sims at 19.2 TFLOPS, avoiding GB300 SXM6's unavailability.
Use Cases
GB300 SXM6's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 enable training massive LLMs without model sharding. A4500's 16 GB GDDR6 cannot accommodate large-scale datasets.
4500 TFLOPS FP8 on GB300 SXM6 delivers ultra-fast inference for high-throughput serving. A4500's 19.2 TFLOPS FP16 falls short for production-scale deployments.
GB300 SXM6 handles fine-tuning of large models with 12000 GB/s bandwidth for big batches. A4500 works for small models but limits scale.
A4500's 16 GB GDDR6 and 19.2 TFLOPS FP16 suffice for image generation at low cost of $0.10 per hour. GB300 SXM6 overkill for typical resolutions.
A4500's 19.2 TFLOPS FP32 fits moderate simulations affordably; GB300 SXM6's 90 TFLOPS FP32 excels in HPC-scale problems needing 288 GB VRAM.
Frequently Asked Questions
What is the VRAM difference between NVIDIA GB300 SXM6 and RTX A4500?▾
GB300 SXM6 offers 288 GB HBM3e VRAM, 18 times more than RTX A4500's 16 GB GDDR6. This enables GB300 SXM6 to load massive AI models entirely in memory. A4500 suits smaller workloads constrained by VRAM.
How do memory bandwidths compare?▾
GB300 SXM6 provides 12000 GB/s, nearly 27 times the RTX A4500's 448 GB/s. Higher bandwidth on GB300 SXM6 supports larger batch sizes in training. A4500 handles standard throughput adequately.
What are the FP16 performance figures?▾
GB300 SXM6 reaches 2250 TFLOPS FP16, over 117 times RTX A4500's 19.2 TFLOPS. This gap accelerates AI training and inference on GB300 SXM6. A4500 performs balanced FP16/FP32 tasks.
Is NVIDIA GB300 SXM6 available for cloud rental?▾
No live offers exist for GB300 SXM6 currently. RTX A4500 rentals start at $0.10 per hour, averaging $0.19 across four providers. GB300 SXM6 targets 2025 datacenter availability.
What are the TDP and form factor differences?▾
GB300 SXM6 has 1400W TDP in SXM form factor with NVLink. RTX A4500 uses 140W TDP in PCIe, suiting lower-power setups. GB300 SXM6 demands robust cooling for clusters.
Which GPU wins for AI training?▾
GB300 SXM6 excels with 2250 TFLOPS FP16 and 288 GB VRAM for large LLMs. RTX A4500's 19.2 TFLOPS limits it to smaller models. Choose based on scale and availability.
Which is cheaper to rent, the GB300 or the RTX A4000?▾
Cloud rental prices for both the GB300 and RTX A4000 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 A4000?▾
The GB300 has 288 GB of HBM3e memory. The RTX A4000 has 16 GB of GDDR6 memory.
Can I find GB300 and RTX A4000 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 A4000?▾
The GB300 uses the Blackwell Ultra architecture (2025) while the RTX A4000 uses Ampere (2021). The GB300 delivers 117.2x the FP16 throughput and 26.8x the memory bandwidth of the RTX A4000.