Specifications Compared
| Spec | A100 | B300 |
|---|---|---|
| TDP | 400W | 1200W |
| VRAM | 40-80 GB | 288 GB |
| CUDA Cores | 6,912 | |
| Memory Type | HBM2e | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | SXM4, PCIe | SXM |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVSwitch, NVLink |
| Tensor Cores | 432 | |
| FP16 Performance | 312 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 90 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 45 TFLOPS |
| INT8 Performance | 624 TOPS | 4,500 TOPS |
| Memory Bandwidth | 2,039 GB/s | 12,000 GB/s |
Performance Analysis
Compute disparities define real-world impacts: B300's 2250 TFLOPS FP16 surpasses A100's 312 TFLOPS by over 7x, slashing LLM training times on large parameter counts. FP32 at 90 TFLOPS versus 19.5 TFLOPS accelerates precision-bound tasks like simulations. FP8 capability of 4500 TFLOPS on B300 optimizes inference for quantized models, reducing latency.
Memory specs transform workflows: 12000 GB/s bandwidth on B300 versus 2039 GB/s on A100 enables batch sizes 5-6x larger, minimizing overhead in training epochs and improving throughput. The 288 GB VRAM handles models exceeding 80 GB limits, avoiding multi-GPU sharding. Higher TDP of 1200W correlates with density in NVSwitch-equipped racks, suiting hyperscale inference.
These metrics translate to efficiency gains: B300 processes FP16 tensors 7x quicker, vital for iterative fine-tuning, while bandwidth uplift sustains data movement for diffusion models.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 80GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 397GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 126GB RAM 1114GB Storage | Czechia | $1.00/GPU/hr $2.00/hr total (2×) | Available | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) | |||
Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
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 | |||
 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 A100 SXM4 80GB
Cost-sensitive deployments select the A100 SXM4 80GB: pricing from $0.13/hr averages $1.23/hr, undercutting B300's $2.45/hr start. Its 400W TDP fits power-capped clusters, unlike 1200W demands.
Ampere-optimized legacy code runs natively on A100 without Blackwell adaptations, ideal for stable production inference on models under 80 GB VRAM.
When to Choose the B300 SXM6
Cutting-edge AI training favors the B300 SXM6: 288 GB VRAM and 12000 GB/s bandwidth manage trillion-parameter LLMs, exceeding A100's 80 GB capacity.
Superior 2250 TFLOPS FP16 and 4500 TFLOPS FP8 deliver rapid iterations and low-latency serving, essential for research and hyperscale services.
Use Cases
B300's 2250 TFLOPS FP16 and 12000 GB/s bandwidth accelerate large-scale training far beyond A100's 312 TFLOPS and 2039 GB/s.
B300's 4500 TFLOPS FP8 and 288 GB VRAM support quantized trillion-parameter models efficiently, unlike A100's 80 GB limit.
Higher 90 TFLOPS FP32 and bandwidth on B300 speed parameter-efficient fine-tuning; A100 suffices for smaller adapters.
B300's VRAM and 12000 GB/s bandwidth handle high-resolution generations without splitting, outperforming A100.
A100's lower $1.23/hr average cost and 19.5 TFLOPS FP32 suit FP32-heavy simulations; B300 overkill for non-AI tasks.
Frequently Asked Questions
Which GPU has more VRAM, A100 or B300?▾
The B300 SXM6 offers 288 GB HBM3e VRAM, compared to 80 GB HBM2e on A100 SXM4 80GB. This enables B300 to load larger models without distribution. Bandwidth follows suit at 12000 GB/s versus 2039 GB/s.
How do FP16 performances compare?▾
B300 achieves 2250 TFLOPS FP16, over 7x the A100's 312 TFLOPS. This boosts training speed for deep learning. FP32 is 90 TFLOPS on B300 versus 19.5 TFLOPS on A100.
What are the cloud rental prices?▾
A100 SXM4 80GB rents from $0.13/hr averaging $1.23/hr across 33 offers. B300 SXM6 starts at $2.45/hr averaging $6.44/hr across 7 offers. Prices reflect availability and demand.
Which has higher power consumption?▾
B300 SXM6 draws 1200W TDP, triple the A100's 400W. This impacts cooling and cluster density. Interconnects include NVSwitch on B300 versus NVLink on A100.
Is B300 better for LLM inference?▾
Yes, B300's 4500 TFLOPS FP8 and 288 GB VRAM excel for large-scale inference. A100 handles smaller models at lower cost. Bandwidth of 12000 GB/s aids high-throughput serving.
When was each GPU released?▾
A100 uses Ampere architecture from 2020. B300 employs Blackwell Ultra from 2025. The five-year gap drives B300's spec leaps like 12000 GB/s bandwidth.
Which is cheaper to rent, the A100 or the B300?▾
Cloud rental prices for both the A100 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 A100 have compared to the B300?▾
The A100 has 40 to 80 GB of HBM2e memory. The B300 has 288 GB of HBM3e memory.
Can I find A100 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 A100 and the B300?▾
The A100 uses the Ampere architecture (2020) while the B300 uses Blackwell Ultra (2025). The B300 delivers 7.2x the FP16 throughput and 5.9x the memory bandwidth of the A100.