A40 vs A5000
Explore a head to head comparison of specifications, performance, and pricing.
A40
The NVIDIA A40 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A5000
The NVIDIA A5000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A40 vs A5000: Which Should You Choose?
The A40 offers 48 GB of VRAM — 2× the 24 GB on the A5000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A40 delivers 37.42 TFLOPS versus 27.77 TFLOPS on the A5000 — 1.3× faster for mixed-precision training and inference. Memory bandwidth favors the A5000 at 0.77 TB/s compared to 0.70 TB/s on the A40, which directly impacts inference latency for memory-bandwidth-bound models. On Shadeform, the A5000 starts from $0.44/hr versus $1.10/hr for the A40 — 150% more expensive — reflecting the performance premium.
A40 — 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 A40 when:
- ✓You need 48 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
A5000 — 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 A5000 when:
- ✓24 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
See how the A40 & A5000 compare
Compare detailed hardware specifications and average pricing for the A40 and A5000.
Compare Hardware Specifications
| A40 | A5000 | |
|---|---|---|
| GPU Type | A40 | A5000 |
| VRAM per GPU | 48 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Ampere |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 696 GB/s | 768 GB/s |
| FP16 TFLOPS | 37.42 TFLOPS (1:1) | 27.77 TFLOPS (1:1) |
| CUDA Cores | 10752 | 8192 |
| Tensor Cores | 336 (3rd Gen) | 256 (3rd Gen) |
| RT Cores | 84 (2nd Gen) | 64 (2nd Gen) |
| Base Clock | 1125 MHz | 1170 MHz |
| Boost Clock | 1740 MHz | 1695 MHz |
| TDP | 300W | 230W |
| Process Node | TSMC 8nm | TSMC 8nm |
| Data Formats | INT8, BF16, FP16, TF32, FP32 | INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| A40 | A5000 | |
|---|---|---|
| 1 GPU | $1.48 /hr | $0.93 /hr |
| 2 GPUs | $2.20 /hr | $1.86 /hr |
| 4 GPUs | $5.92 /hr | $3.72 /hr |
| 8 GPUs | $8.80 /hr | $3.52 /hr |
Frequently Asked Questions: A40 vs A5000
The main differences are VRAM (48 GB vs 24 GB), FP16 throughput (37.42 vs 27.77 TFLOPS).
The A40 is generally better for large language model training due to its higher throughput and 48 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 A5000 is available from $0.44/hr. The A40 starts from $1.10/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The A40 has more VRAM at 48 GB, compared to 24 GB on the A5000. 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 A5000 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 A40, paying the premium may be justified by faster job completion and lower total cost.
The A40 is currently available across 2 cloud providers on Shadeform's network, compared to 2 for the A5000. 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 A40 or A5000. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A40 & A5000 Instances
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