A4000 vs RTX 4090
Explore a head to head comparison of specifications, performance, and pricing.
A4000
The NVIDIA A4000 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
RTX 4090
The NVIDIA RTX 4090 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A4000 vs RTX 4090: Which Should You Choose?
The RTX 4090 offers 24 GB of VRAM — 1.5× the 16 GB on the A4000 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the RTX 4090 delivers 82.58 TFLOPS versus 19.17 TFLOPS on the A4000 — 4× faster for mixed-precision training and inference. Memory bandwidth favors the A4000 at 0.45 TB/s compared to 0.00 TB/s on the RTX 4090, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A4000 is built on Ampere while the RTX 4090 uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the A4000 starts from $0.15/hr versus $0.60/hr for the RTX 4090 — 300% more expensive — reflecting the performance premium. The RTX 4090 is available across 3 cloud providers on Shadeform compared to 2 for the A4000, giving more options for region and pricing flexibility.
A4000 — 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 A4000 when:
- ✓16 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
RTX 4090 — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4090 when:
- ✓You need 24 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
- ✓You need flexibility across multiple cloud providers or regions
See how the A4000 & RTX 4090 compare
Compare detailed hardware specifications and average pricing for the A4000 and RTX 4090.
Compare Hardware Specifications
| A4000 | RTX 4090 | |
|---|---|---|
| GPU Type | A4000 | RTX 4090 |
| VRAM per GPU | 16 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Ada Lovelace |
| Interconnect | PCIe Gen4 | PCIe Gen4 |
| Memory Bandwidth | 448 GB/s | 1.008 TB/s |
| FP16 TFLOPS | 19.17 TFLOPS (1:1) | 82.58 TFLOPS (1:1) |
| CUDA Cores | 6144 | 16384 |
| Tensor Cores | 192 (3rd Gen) | 512 (4th Gen) |
| RT Cores | 48 (2nd Gen) | 128 (3rd Gen) |
| Base Clock | 735 MHz | 2235 MHz |
| Boost Clock | 1695 MHz | 2520 MHz |
| TDP | 140W | 450W |
| Process Node | TSMC 8nm | TSMC 4N |
| Data Formats | INT8, BF16, FP16, TF32, FP32 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| A4000 | RTX 4090 | |
|---|---|---|
| 1 GPU | $0.47 /hr | $0.60 /hr |
| 2 GPUs | $0.95 /hr | $1.20 /hr |
| 4 GPUs | $1.90 /hr | $2.40 /hr |
| 8 GPUs | $1.20 /hr | $3.97 /hr |
Frequently Asked Questions: A4000 vs RTX 4090
The main differences are VRAM (16 GB vs 24 GB), FP16 throughput (19.17 vs 82.58 TFLOPS), architecture (Ampere vs Ada Lovelace). The A4000 uses the Ampere architecture while the RTX 4090 is based on Ada Lovelace, giving each GPU different generational capabilities.
The RTX 4090 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 A4000 is available from $0.15/hr. The RTX 4090 starts from $0.60/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The RTX 4090 has more VRAM at 24 GB, compared to 16 GB on the A4000. 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 RTX 4090 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 A4000, paying the premium may be justified by faster job completion and lower total cost.
The RTX 4090 is currently available across 3 cloud providers on Shadeform's network, compared to 2 for the A4000. 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 A4000 or RTX 4090. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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