Specifications Compared
| Spec | A30 | B300 |
|---|---|---|
| TDP | 165W | 1200W |
| VRAM | 24 GB | 288 GB |
| CUDA Cores | 3,584 | |
| Memory Type | HBM2 | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | PCIe | SXM |
| Interconnect | NVLink | NVSwitch, NVLink |
| Tensor Cores | 224 | |
| FP16 Performance | 10.3 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 90 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 45 TFLOPS |
| INT8 Performance | 165 TOPS | 4,500 TOPS |
| Memory Bandwidth | 933 GB/s | 12,000 GB/s |
Performance Analysis
The B300 vastly outpaces the A30 in compute capabilities: FP16 performance surges to 2250 TFLOPS from 10.3 TFLOPS, accelerating AI training and inference by over 200 times. FP32 improves to 90 TFLOPS, benefiting general-purpose computing tasks. The FP16 to FP32 balance on the A30 stays equal at 10.3 TFLOPS each, suitable for balanced workloads, whereas the B300's FP8 at 4500 TFLOPS optimizes low-precision inference for massive models.
Memory differences transform real-world usage. The B300's 288 GB HBM3e VRAM versus 24 GB HBM2 enables handling models exceeding 100 billion parameters without multi-GPU sharding. Its 12000 GB/s bandwidth supports larger batch sizes, reducing training time by minimizing data bottlenecks compared to the A30's 933 GB/s limit. Higher TDP at 1200W on the B300 demands robust cooling, unlike the A30's efficient 165W.
Interconnects further differentiate them. NVLink on the A30 scales modestly, while NVSwitch and NVLink on the B300 enable seamless multi-GPU clusters for distributed training.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA B300 SXM6 262GB VRAM | 262GB | 0 vCPU 0GB RAM | 🌍global | $7.39/GPU/hr | |||
 VERDA | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 240 vCPU 2040GB RAM | Helsinki | $7.50/GPU/hr $60.00/hr total (8×) | Available | ||
 Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
When to Choose the A30
The A30 fits scenarios with strict power budgets: its 165W TDP consumes far less energy than the B300's 1200W. PCIe form factor integrates easily into standard servers without specialized infrastructure. For workloads like fine-tuning smaller models under 24 GB VRAM or legacy inference at 10.3 TFLOPS FP16, the A30 delivers adequate performance where on-premises hardware exists, despite lacking live cloud offers.
When to Choose the B300
Opt for the B300 in high-scale AI deployments: 288 GB VRAM handles enormous models, and 12000 GB/s bandwidth sustains large batches. FP16 at 2250 TFLOPS and FP8 at 4500 TFLOPS excel in training and inference for LLMs over 100B parameters. SXM form factor with NVSwitch supports clusters, available from $6.94 per hour across six providers.
Use Cases
B300's 2250 TFLOPS FP16 and 288 GB VRAM support training models over 100B parameters with large batches. A30's 10.3 TFLOPS and 24 GB limit it to smaller scales.
FP8 at 4500 TFLOPS and 12000 GB/s bandwidth on B300 enable high-throughput serving of massive LLMs. A30's 933 GB/s bandwidth constrains batch sizes.
B300's 90 TFLOPS FP32 and 288 GB VRAM handle large datasets efficiently. A30 suffices only for models under 24 GB.
B300's high FP16 and memory bandwidth accelerate image generation at scale. A30 works for basic tasks but slows on high-res outputs.
B300's 90 TFLOPS FP32 outperforms A30's 10.3 TFLOPS for simulations. Vast VRAM aids complex datasets.
Frequently Asked Questions
Which GPU has more VRAM?▾
The B300 provides 288 GB HBM3e VRAM, compared to the A30's 24 GB HBM2. This 12-fold increase allows the B300 to load much larger AI models without splitting across GPUs.
What is the memory bandwidth difference?▾
B300 offers 12000 GB/s, over 12 times the A30's 933 GB/s. Higher bandwidth on B300 supports bigger batch sizes and faster data transfers in training.
How do FP16 performances compare?▾
B300 achieves 2250 TFLOPS FP16, versus A30's 10.3 TFLOPS. This gap exceeds 200 times, making B300 ideal for AI acceleration.
What are the power requirements?▾
A30 uses 165W TDP, while B300 requires 1200W. A30 suits low-power setups; B300 needs advanced cooling.
Is the B300 available in the cloud?▾
B300 has live offers from $6.94 per hour, averaging $7.11 across six providers. A30 currently lacks cloud availability.
Which is better for LLM inference?▾
B300 excels with 4500 TFLOPS FP8 and 288 GB VRAM for high-throughput serving. A30's specs limit it to smaller models.
Which is cheaper to rent, the A30 or the B300?▾
Cloud rental prices for both the A30 and B300 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 A30 have compared to the B300?▾
The A30 has 24 GB of HBM2 memory. The B300 has 288 GB of HBM3e memory.
Can I find A30 and B300 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 A30 and the B300?▾
The A30 uses the Ampere architecture (2021) while the B300 uses Blackwell Ultra (2025). The B300 delivers 218.4x the FP16 throughput and 12.9x the memory bandwidth of the A30.