Specifications Compared
| Spec | B300 | RTX-A2000 |
|---|---|---|
| TDP | 1200W | 70W |
| VRAM | 288 GB | 6-12 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 | 8 TFLOPS |
| FP32 Performance | 90 TFLOPS | 8 TFLOPS |
| FP64 Performance | 45 TFLOPS | |
| INT8 Performance | 4,500 TOPS | |
| Memory Bandwidth | 12,000 GB/s | 288 GB/s |
Performance Analysis
Compute disparities define these GPUs' capabilities: B300 achieves 2250 TFLOPS in FP16 and 90 TFLOPS in FP32, dwarfing A2000's matched 8 TFLOPS in both formats. This gap accelerates AI model training on B300, where FP16 tensor cores handle matrix multiplications 281 times faster, reducing epochs from days to hours for large datasets. FP32 parity on A2000 limits it to basic simulations, while B300's 4500 TFLOPS FP8 excels in quantized inference. Memory specs further separate them: B300's 288 GB HBM3e and 12000 GB/s bandwidth enable batch sizes exceeding thousands for LLMs, preventing out-of-memory errors common on A2000's 6-12 GB GDDR6 at 288 GB/s. Larger batches on B300 optimize throughput in training and inference pipelines. Power draw underscores efficiency trade-offs: B300's 1200W TDP suits datacenters, versus A2000's 70W for edge or desktop use. Real-world implications favor B300 for deep learning: its specs support models over 100B parameters, while A2000 handles prototyping under 1B parameters.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300 SXM6
| 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 |
RTX A2000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
RunPod | NVIDIA RTX A2000 12GB VRAM | 12GB | 6 vCPU 20GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the B300 SXM6
NVIDIA B300 SXM6 excels in large-scale AI training and inference: its 288 GB VRAM and 2250 TFLOPS FP16 performance manage models exceeding 12 GB, impossible on RTX A2000. Datacenter users leverage NVLink interconnects for multi-GPU scaling at $2.45 per hour starting price.
When to Choose the RTX A2000
NVIDIA RTX A2000 suits budget prototyping and light inference: 6-12 GB VRAM and 8 TFLOPS FP16 handle small models or Stable Diffusion at $0.06 per hour. Low 70W TDP fits laptops or low-power clouds without cluster needs.
Use Cases
B300's 288 GB VRAM and 2250 TFLOPS FP16 enable training models over 100B parameters; A2000's 6-12 GB and 8 TFLOPS cannot load such datasets.
B300's 4500 TFLOPS FP8 and 12000 GB/s bandwidth support high-batch inference for large LLMs; A2000 limits to tiny models.
B300 handles fine-tuning on full datasets with 90 TFLOPS FP32; A2000's 8 TFLOPS FP32 restricts to micro-tuning small models.
A2000's 6-12 GB VRAM suffices for image generation at 8 TFLOPS FP16; B300 overkill for single-instance tasks.
A2000 works for FP32 simulations under 8 TFLOPS on modest data; B300 accelerates HPC with 90 TFLOPS FP32 and NVSwitch.
Frequently Asked Questions
What is the VRAM difference between B300 SXM6 and RTX A2000?▾
B300 SXM6 provides 288 GB HBM3e VRAM; RTX A2000 offers 6-12 GB GDDR6. This 24-48 times gap allows B300 to load massive AI models without swapping.
How do cloud prices compare for these GPUs?▾
B300 SXM6 starts at $2.45 per hour averaging $6.44 across 7 offers; RTX A2000 from $0.06 per hour averaging $0.23 across 3 offers. Pricing scales with 2250 TFLOPS versus 8 TFLOPS performance.
Is B300 better for AI training than A2000?▾
Yes: B300's 2250 TFLOPS FP16 outperforms A2000's 8 TFLOPS by 281 times. Combined with 12000 GB/s bandwidth, it trains LLMs far faster.
What is the power consumption difference?▾
B300 SXM6 requires 1200W TDP for datacenter use; RTX A2000 uses 70W for efficient workstations. A2000 suits low-power environments.
Can RTX A2000 handle large model inference?▾
No: 6-12 GB VRAM limits it to models under 7B parameters; B300's 288 GB supports 100B+ models at 4500 TFLOPS FP8.
What architectures do they use?▾
B300 SXM6 employs Blackwell Ultra from 2025; RTX A2000 uses Ampere from 2021. Blackwell delivers 90 TFLOPS FP32 versus Ampere's 8 TFLOPS.
Which is cheaper to rent, the B300 or the RTX A2000?▾
Cloud rental prices for both the B300 and RTX A2000 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 B300 have compared to the RTX A2000?▾
The B300 has 288 GB of HBM3e memory. The RTX A2000 has 6 to 12 GB of GDDR6 memory.
Can I find B300 and RTX A2000 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 B300 and the RTX A2000?▾
The B300 uses the Blackwell Ultra architecture (2025) while the RTX A2000 uses Ampere (2021). The B300 delivers 281.3x the FP16 throughput and 41.7x the memory bandwidth of the RTX A2000.