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
FP16 performance defines AI acceleration: the GB300 achieves 2250 TFLOPS, over 117 times the A4000's 19.2 TFLOPS, slashing training times for large neural networks reliant on half-precision. FP32 at 90 TFLOPS on GB300 exceeds A4000's 19.2 TFLOPS by 4.7 times, benefiting simulation tasks, though A4000's parity in FP16 and FP32 suits graphics rendering. GB300's FP8 capability of 4500 TFLOPS optimizes quantized inference, unavailable on A4000. Memory bandwidth of 12000 GB/s on GB300 versus 448 GB/s supports massive batch sizes in transformer models, enabling efficient scaling to trillion-parameter LLMs; A4000's limit constrains it to smaller datasets. VRAM disparity, 288 GB versus 16 GB, prevents A4000 from loading modern foundation models intact.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX A4000
| 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
Select the GB300 for exascale AI training or inference: 288 GB HBM3e VRAM accommodates models over 500 billion parameters, and 2250 TFLOPS FP16 reduces epochs from weeks to hours. Its 12000 GB/s bandwidth sustains large batches in distributed setups via NVLink. Datacenter clusters demand this over workstation alternatives.
When to Choose the RTX A4000
The RTX A4000 fits cost-sensitive visualization and prototyping: 16 GB GDDR6 handles 4K rendering or fine-tuning under 7 billion parameters at $0.08 per hour. Low 140W TDP enables desktop or edge deployments without cooling infrastructure. It avoids overkill for non-AI tasks.
Use Cases
GB300's 288 GB VRAM and 2250 TFLOPS FP16 handle trillion-parameter models with large batches via 12000 GB/s bandwidth. A4000's 16 GB limits it to tiny models.
4500 TFLOPS FP8 on GB300 accelerates quantized serving at scale. A4000's 19.2 TFLOPS FP16 cannot match throughput for production.
GB300 suits large models with 288 GB VRAM; A4000 works for sub-7B parameters at $0.08 per hour. Choice depends on model size.
A4000's 19.2 TFLOPS FP16 and 16 GB GDDR6 suffice for image generation at low cost. GB300 overpowers routine creative tasks.
GB300's 90 TFLOPS FP32 and 12000 GB/s bandwidth excel in simulations with massive datasets. A4000 limits complex HPC runs.
Frequently Asked Questions
What is the VRAM difference between GB300 and RTX A4000?▾
GB300 offers 288 GB HBM3e, 18 times more than A4000's 16 GB GDDR6. This enables GB300 to load enormous AI models whole. A4000 requires model parallelism for larger tasks.
How do FP16 performances compare?▾
GB300 delivers 2250 TFLOPS FP16, exceeding A4000's 19.2 TFLOPS by 117 times. Training accelerates dramatically on GB300. A4000 suits lighter deep learning.
What are the power requirements?▾
GB300 demands 1400W TDP in SXM form, needing datacenter cooling. A4000 uses 140W in PCIe, fitting workstations. Power scales with performance.
Is RTX A4000 available in the cloud?▾
RTX A4000 pricing starts at $0.08 per hour, averaging $0.37 across 28 offers. GB300 has no live cloud availability yet. A4000 provides immediate access.
Which has higher memory bandwidth?▾
GB300 achieves 12000 GB/s, 27 times A4000's 448 GB/s. This supports bigger batches on GB300. A4000 bottlenecks large data flows.
What architectures do they use?▾
GB300 employs Blackwell Ultra from 2025; A4000 uses Ampere from 2021. Generational leap boosts GB300's AI specs. A4000 remains viable for legacy code.
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.