V100 vs RTX 4090
Explore a head to head comparison of specifications, performance, and pricing.
V100
The NVIDIA V100 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.
V100 vs RTX 4090: Which Should You Choose?
The RTX 4090 offers 24 GB of VRAM — 1.5× the 16 GB on the V100 — 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 28.26 TFLOPS on the V100 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the V100 at 0.90 TB/s compared to 0.00 TB/s on the RTX 4090, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the V100 is built on Volta while the RTX 4090 uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the V100 starts from $0.39/hr versus $0.60/hr for the RTX 4090 — 54% more expensive — reflecting the performance premium.
V100 — Best Use Cases
- •Deep learning training
- •HPC and scientific computing
- •Legacy ML infrastructure
Choose V100 when:
- ✓16 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
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
See how the V100 & RTX 4090 compare
Compare detailed hardware specifications and average pricing for the V100 and RTX 4090.
Compare Hardware Specifications
| V100 | RTX 4090 | |
|---|---|---|
| GPU Type | V100 | RTX 4090 |
| VRAM per GPU | 16 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Volta | Ada Lovelace |
| Interconnect | PCIe Gen3 | PCIe Gen4 |
| Memory Bandwidth | 900 GB/s | 1.008 TB/s |
| FP16 TFLOPS | 28.26 TFLOPS (2:1) | 82.58 TFLOPS (1:1) |
| CUDA Cores | 5120 | 16384 |
| Tensor Cores | 640 (1st Gen) | 512 (4th Gen) |
| RT Cores | N/A | 128 (3rd Gen) |
| Base Clock | 1230 MHz | 2235 MHz |
| Boost Clock | 1380 MHz | 2520 MHz |
| TDP | 250-300W | 450W |
| Process Node | TSMC 12nm | TSMC 4N |
| Data Formats | FP16, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| V100 | RTX 4090 | |
|---|---|---|
| 1 GPU | $1.36 /hr | $0.60 /hr |
| 2 GPUs | $0.78 /hr | $1.20 /hr |
| 4 GPUs | N/A | $2.40 /hr |
| 8 GPUs | $3.76 /hr | $3.97 /hr |
Frequently Asked Questions: V100 vs RTX 4090
The main differences are VRAM (16 GB vs 24 GB), FP16 throughput (28.26 vs 82.58 TFLOPS), architecture (Volta vs Ada Lovelace). The V100 uses the Volta 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 V100 is available from $0.39/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 V100. 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 V100, paying the premium may be justified by faster job completion and lower total cost.
The V100 is currently available across 3 cloud providers on Shadeform's network, compared to 3 for the RTX 4090. 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 V100 or RTX 4090. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore V100 & RTX 4090 Instances
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