Specifications Compared
| Spec | A16 | GB300 |
|---|---|---|
| TDP | 250W | 1400W |
| VRAM | 16 GB | 288 GB |
| CUDA Cores | 2,560 | |
| Memory Type | GDDR6 | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | PCIe | SXM |
| Interconnect | NVSwitch, NVLink | |
| Tensor Cores | 80 | |
| FP16 Performance | 4.5 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 4.5 TFLOPS | 90 TFLOPS |
| Memory Bandwidth | 231 GB/s | 12,000 GB/s |
Performance Analysis
The FP16 performance gap defines inference dominance: A16 achieves 4.5 TFLOPS while GB300 reaches 2250 TFLOPS, enabling the latter to process vastly larger batches or models in real time. FP8 support at 4500 TFLOPS on GB300 further accelerates quantized inference common in LLMs. For training, FP32 matters more: A16's 4.5 TFLOPS contrasts with GB300's 90 TFLOPS, a 20x advantage that shortens epochs for massive datasets. Memory bandwidth profoundly impacts batch sizes: 231 GB/s on A16 limits handling of large inputs, whereas 12000 GB/s on GB300 supports enormous batches without bottlenecks, ideal for transformer models exceeding 16 GB VRAM. Overall, GB300 excels in memory-intensive tasks, reducing latency by orders of magnitude.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A16
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | 8×NVIDIA A16 64GB VRAM | 64GB | 48 vCPU 496GB RAM 1500GB Storage | Singapore | $0.47/GPU/hr $3.77/hr total (8×) | Available | ||
Vultr | 8×NVIDIA A16 64GB VRAM | 64GB | 48 vCPU 496GB RAM 1500GB Storage | Atlanta | $0.47/GPU/hr $3.77/hr total (8×) | Available | ||
Vultr | 8×NVIDIA A16 64GB VRAM | 64GB | 48 vCPU 496GB RAM 1500GB Storage | Bangalore | $0.47/GPU/hr $3.77/hr total (8×) | Available | ||
Vultr | 2×NVIDIA A16 64GB VRAM | 64GB | 12 vCPU 128GB RAM 700GB Storage | Bangalore | $0.47/GPU/hr $0.94/hr total (2×) | Available | ||
Vultr | 4×NVIDIA A16 64GB VRAM | 64GB | 24 vCPU 256GB RAM 1200GB Storage | Atlanta | $0.47/GPU/hr $1.88/hr total (4×) | Available |
When to Choose the A16
The A16 excels in cost-sensitive inference for smaller models fitting within 16 GB GDDR6 VRAM. Its PCIe form factor enables easy multi-GPU scaling in standard cloud instances, and 250W TDP keeps power costs low compared to 1400W alternatives. With pricing from $0.47 per hour across 75 offers, it suits production deployments needing immediate availability without overprovisioning.
When to Choose the GB300 SXM6
The GB300 dominates large-scale AI workloads requiring 288 GB HBM3e VRAM and 12000 GB/s bandwidth. NVLink and NVSwitch interconnects facilitate cluster-scale training, leveraging 2250 TFLOPS FP16 for rapid LLM inference. Despite 1400W TDP and lack of current pricing, it future-proofs high-performance computing environments.
Use Cases
GB300's 90 TFLOPS FP32 outperforms A16's 4.5 TFLOPS by 20x, accelerating large dataset epochs. Its 288 GB VRAM handles massive models infeasible on 16 GB.
GB300's 2250 TFLOPS FP16 and 4500 TFLOPS FP8 enable low-latency serving of huge LLMs. Bandwidth of 12000 GB/s supports large batches unlike A16's 231 GB/s.
GB300's superior FP16 at 2250 TFLOPS and 288 GB VRAM speed up parameter updates on large models. A16 limits scale with 16 GB and 4.5 TFLOPS.
GB300 processes high-resolution generations faster via 12000 GB/s bandwidth and ample VRAM. A16 suffices for basic tasks but bottlenecks on complex prompts.
GB300's 90 TFLOPS FP32 and NVLink interconnect excel in simulations needing high precision. A16's 4.5 TFLOPS FP32 restricts complex computations.
Frequently Asked Questions
What is the VRAM difference between A16 and GB300?▾
A16 provides 16 GB GDDR6 VRAM while GB300 offers 288 GB HBM3e, an 18x increase. This allows GB300 to load much larger models without swapping. Bandwidth follows suit at 231 GB/s versus 12000 GB/s.
How do FP16 performances compare?▾
A16 delivers 4.5 TFLOPS FP16, but GB300 achieves 2250 TFLOPS, over 500x higher. This gap favors GB300 for inference-heavy AI tasks. FP8 on GB300 adds 4500 TFLOPS for quantization.
What are the power requirements?▾
A16 has a 250W TDP suitable for efficient deployments. GB300 demands 1400W, reflecting its performance scale. Form factors differ: PCIe for A16, SXM for GB300.
Is A16 available for cloud rental?▾
A16 pricing starts at $0.47 per hour, averaging $0.48 across 75 live offers. GB300 has no live offers yet due to its 2025 release. A16 provides immediate access.
Which is better for LLM inference?▾
GB300 excels with 2250 TFLOPS FP16 and 288 GB VRAM for large models. A16 works for smaller ones at 4.5 TFLOPS but limits batch sizes via 231 GB/s bandwidth.
What interconnects do they use?▾
A16 relies on PCIe without advanced links. GB300 features NVSwitch and NVLink for multi-GPU scaling. This boosts GB300 in cluster environments.
Which is cheaper to rent, the A16 or the GB300?▾
Cloud rental prices for both the A16 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 A16 have compared to the GB300?▾
The A16 has 16 GB of GDDR6 memory. The GB300 has 288 GB of HBM3e memory.
Can I find A16 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 A16 and the GB300?▾
The A16 uses the Ampere architecture (2021) while the GB300 uses Blackwell Ultra (2025). The GB300 delivers 500.0x the FP16 throughput and 51.9x the memory bandwidth of the A16.