A16 vs A30
Explore a head to head comparison of specifications, performance, and pricing.
A16
The NVIDIA A16 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A30
The NVIDIA A30 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A16 vs A30: Which Should You Choose?
The A16 offers 64 GB of VRAM — 3× the 24 GB on the A30 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A30 delivers 10.32 TFLOPS versus 4.493 TFLOPS on the A16 — 2× faster for mixed-precision training and inference. Memory bandwidth favors the A30 at 0.93 TB/s compared to 0.00 TB/s on the A16, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the A30 starts from $0.35/hr versus $0.51/hr for the A16 — 46% more expensive — reflecting the performance premium.
A16 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A16 when:
- ✓You need 64 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓Your workload does not require peak FP16 throughput
A30 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A30 when:
- ✓24 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
See how the A16 & A30 compare
Compare detailed hardware specifications and average pricing for the A16 and A30.
Compare Hardware Specifications
| A16 | A30 | |
|---|---|---|
| GPU Type | A16 | A30 |
| VRAM per GPU | 64 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Ampere |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 4x 200 GB/s | 933 GB/s |
| FP16 TFLOPS | 4.493 TFLOPS (1:1) | 10.32 TFLOPS (1:1) |
| CUDA Cores | 4x 1,280 | 3584 |
| Tensor Cores | 4x 40 (3rd Gen) | 224 (3rd Gen) |
| RT Cores | 4x 10 (2nd Gen) | N/A |
| Base Clock | 1312 MHz | 930 MHz |
| Boost Clock | 1755 MHz | 1440 MHz |
| TDP | 250W | 165W |
| Process Node | TSMC 8nm | TSMC 7nm |
| Data Formats | INT8, BF16, FP16, TF32, FP32 | INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| A16 | A30 | |
|---|---|---|
| 1 GPU | $0.51 /hr | $0.35 /hr |
| 2 GPUs | $1.02 /hr | $0.70 /hr |
| 4 GPUs | $2.05 /hr | $1.40 /hr |
| 8 GPUs | $4.09 /hr | $2.80 /hr |
Frequently Asked Questions: A16 vs A30
The main differences are VRAM (64 GB vs 24 GB), FP16 throughput (4.493 vs 10.32 TFLOPS).
The A30 is generally better for large language model training due to its higher throughput and 24 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the A30 is available from $0.35/hr. The A16 starts from $0.51/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The A16 has more VRAM at 64 GB, compared to 24 GB on the A30. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the A30 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A16, paying the premium may be justified by faster job completion and lower total cost.
The A16 is currently available across 1 cloud providers on Shadeform's network, compared to 1 for the A30. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either A16 or A30. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A16 & A30 Instances
Browse available instances with A16 and A30 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.
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