Specifications Compared
| Spec | A40 | GB300 |
|---|---|---|
| TDP | 300W | 1400W |
| VRAM | 48 GB | 288 GB |
| CUDA Cores | 10,752 | |
| Memory Type | GDDR6 | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | PCIe | SXM |
| Interconnect | NVLink | NVSwitch, NVLink |
| Tensor Cores | 336 | |
| FP16 Performance | 37.4 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 37.4 TFLOPS | 90 TFLOPS |
| FP64 Performance | 0.6 TFLOPS | 45 TFLOPS |
| INT8 Performance | 299 TOPS | 4,500 TOPS |
| Memory Bandwidth | 696 GB/s | 12,000 GB/s |
Performance Analysis
Raw specifications reveal profound disparities: the GB300's 2250 TFLOPS FP16 dwarfs the A40's 37.4 TFLOPS, accelerating AI training where half-precision dominates. The A40 maintains parity at 37.4 TFLOPS FP32, ideal for precision-bound simulations, but the GB300's 90 TFLOPS FP32 still advances throughput. Introduction of 4500 TFLOPS FP8 on GB300 optimizes inference for quantized LLMs, enabling higher servings per watt despite 1400W TDP versus A40's efficient 300W.
Memory bandwidth defines real-world viability: 12000 GB/s on GB300 supports enormous batch sizes in training, fitting models exceeding 48 GB VRAM into 288 GB without swapping. The A40's 696 GB/s limits scale for large language models, causing bottlenecks in data loading. HBM3e versus GDDR6 further enhances GB300's speed for memory-intensive tasks like fine-tuning, where sustained 12000 GB/s prevents stalls.
Interconnects amplify this: NVSwitch on GB300 enables cluster-scale multi-GPU training, surpassing A40's NVLink for PCIe setups. Power scaling reflects intent: A40 suits dense general-purpose racks, GB300 demands specialized cooling for peak AI factories.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A40
| 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 | 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 | ||
![]() Vast.ai | 8×NVIDIA RTX A4000 16GB VRAM | 16GB | 80 vCPU 315GB RAM 2313GB Storage | United Kingdom | $0.16/GPU/hr $1.28/hr total (8×) | Available |
When to Choose the A40
Select the A40 for cost-sensitive deployments requiring immediate availability. With cloud pricing from $0.24 per hour across 23 offers, it delivers 37.4 TFLOPS FP32 for scientific computing and visualization without delay. Its 300W TDP and PCIe form factor integrate into standard servers, avoiding the GB300's unavailable status and 1400W demands.
The A40 excels in balanced workloads like CAD or moderate ML inference, where 48 GB VRAM and 696 GB/s bandwidth suffice without overprovisioning power or awaiting 2025 hardware.
When to Choose the GB300
Choose the GB300 for frontier AI research demanding extreme scale. Its 288 GB HBM3e VRAM and 12000 GB/s bandwidth handle trillion-parameter models, with 2250 TFLOPS FP16 slashing training times versus A40's limits.
SXM form factor with NVSwitch supports massive clusters, ideal for hyperscale inference at 4500 TFLOPS FP8 once deployed, prioritizing performance over the A40's current $1.26 per hour average.
Use Cases
GB300's 288 GB VRAM and 2250 TFLOPS FP16 support trillion-parameter models with 12000 GB/s bandwidth for large batches. A40's 48 GB limits scale.
4500 TFLOPS FP8 and 12000 GB/s bandwidth deliver massive throughput for quantized serving. A40's 37.4 TFLOPS FP16 cannot match volume.
288 GB HBM3e fits full models for efficient tuning at 2250 TFLOPS FP16. A40 requires model parallelism due to 48 GB constraint.
GB300's high memory bandwidth and FP16 performance accelerate high-resolution generation. A40 handles smaller scales but bottlenecks at 696 GB/s.
A40's balanced 37.4 TFLOPS FP32/FP16 and 300W TDP suit simulations with low cost from $0.24 per hour. GB300 overkill for precision tasks.
Frequently Asked Questions
What is the VRAM capacity of A40 versus GB300?▾
The A40 provides 48 GB GDDR6 VRAM. The GB300 offers 288 GB HBM3e, enabling larger models without partitioning.
Which GPU has higher FP16 performance?▾
GB300 achieves 2250 TFLOPS FP16. A40 delivers 37.4 TFLOPS, a 60x gap favoring GB300 for AI training.
How do power requirements compare?▾
A40 consumes 300W TDP in PCIe form. GB300 requires 1400W in SXM, demanding advanced cooling.
What are the current cloud prices for these GPUs?▾
A40 starts at $0.24 per hour, averaging $1.26 across 23 offers. GB300 has no live cloud offers available.
What architectures power these GPUs?▾
A40 uses Ampere from 2020. GB300 employs Blackwell Ultra for 2025, with NVSwitch interconnect.
How does memory bandwidth differ?▾
A40 bandwidth is 696 GB/s. GB300 reaches 12000 GB/s, supporting 17x larger batches in memory-bound tasks.
Which is cheaper to rent, the A40 or the GB300?▾
Cloud rental prices for both the A40 and GB300 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 A40 have compared to the GB300?▾
The A40 has 48 GB of GDDR6 memory. The GB300 has 288 GB of HBM3e memory.
Can I find A40 and GB300 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 A40 and the GB300?▾
The A40 uses the Ampere architecture (2020) while the GB300 uses Blackwell Ultra (2025). The GB300 delivers 60.2x the FP16 throughput and 17.2x the memory bandwidth of the A40.


