Data Center NVIDIA

NVIDIA V100 32GB

Volta Architecture · 32GB HBM2 · SXM

VRAM
32GB
FP16
31.4
TDP
300W
Hardware Price
$2.5k
Cloud from
$0.140/hr
5 providers

Quick Insights

Performance/Dollar
12.56 TFLOPS/$k
FP16 performance per $1000
VRAM/Dollar
12.8 GB/$k
VRAM per $1000
vs Data Center Average
-83% perf
FP16 TFLOPS comparison
Cloud Availability
5 providers
from $0.140/hr

Specifications

VRAM 32GB HBM2
Memory Bandwidth 900 GB/s
FP16 TFLOPS 31.4
Tensor TFLOPS 125.0
FP32 TFLOPS 15.7
TDP 300W
Form Factor SXM
Architecture Volta
NVLink Yes (300GB/s)
Release Date 2018-03

V100 Price and Rental Notes

NVIDIA V100 32GB searches have a clearer memory-size intent than generic Tesla V100 queries. This page should answer whether the 32GB variant is still useful, how to compare used prices, and when rental is safer.

V100 32GB price and memory fit

The 32GB V100 is more flexible than 16GB V100 for older training jobs, embeddings, and batch inference. Still, many modern LLM and diffusion workflows may benefit more from A100 40GB/80GB, L40S, or H100.

Used V100 32GB buying checks

Check PCIe vs SXM form factor, cooling, server compatibility, power draw, and memory health. A cheaper 32GB card can become expensive if it needs a specialized chassis or has no credible warranty path.

Rental alternative

If the project is short or experimental, compare the used-card quote with a rental budget for V100, A100, or L4 capacity. Rental keeps depreciation and failure risk off your balance sheet.

Cost decision checks for V100 buyers and renters.
Decision factor Buy or rent signal Cost check
32GB VRAM ceiling V100 32GB fits older models and some inference jobs, but larger models may need quantization or a newer GPU. Estimate whether reduced batch size will increase runtime enough to erase the hardware discount.
PCIe vs SXM listing Treat PCIe and SXM listings separately because server compatibility and cooling are very different. Do not compare a bare SXM module with a ready-to-run PCIe card without adding host-system cost.
Long-term lab use Buying can make sense for a lab that will keep the card busy and can maintain the server. Include power, cooling, downtime, and replacement risk in the monthly equivalent cost.

V100 cost planning checklist

  • Before buying, confirm the exact V100 32GB form factor, seller warranty, memory test status, and driver compatibility with the software stack you need.
  • Benchmark a representative job on rented V100 or A100 capacity if possible, then compare total job cost rather than headline used price.
  • If the model barely fits in 32GB, price a larger GPU alternative as well. A higher hourly rate can still be cheaper when it avoids sharding or repeated failed runs.

Reference checks: NVIDIA Tesla V100 specifications · NVIDIA V100 installation guidance

Buy vs Rent Analysis

Buy Hardware
$2.5k
  • One-time cost, unlimited usage
  • Full control over hardware
  • Electricity & cooling costs extra
  • Depreciation over 2-3 years
Best if using >17857 hours total
Rent Cloud GPU
$0.140/hr
  • Pay only for what you use
  • No upfront investment
  • Scale up/down instantly
  • No maintenance required
Best for <17857 hours or variable usage
Breakeven Point
17,857
hours of usage

At $0.140/hr cloud pricing, buying the hardware pays off after 17,857 hours (~744 days or 24.8 months of 24/7 usage).

Usage Monthly Cloud Cost Months to Breakeven
100 hrs/month $14.00 179 months
200 hrs/month $28.00 90 months
500 hrs/month $70.00 36 months

Cloud GPU Pricing

Rent NVIDIA V100 32GB from 5 cloud providers. Prices shown per GPU per hour.

Provider Type Instance GPUs On-Demand Per GPU Spot Availability
Datacrunch (Verda) gpu-cloud datacrunch-v100-32gb 1x $0.140/hr $0.140/hr Cheapest - -
TensorDock marketplace tensordock-v100-32gb 1x $0.170/hr $0.170/hr - -
CoreWeave gpu-cloud coreweave-v100-32gb 1x $0.800/hr $0.800/hr - -
Paperspace gpu-cloud paperspace-v100-32gb 1x $2.30/hr $2.30/hr - -
Google Cloud Platform hyperscaler gcp-v100 1x $2.48/hr $2.48/hr $0.992/hr (-60%) -
Best Spot Deal: Google Cloud Platform offers spot pricing at $0.992/hr (60% off on-demand).

V100 vs Alternatives

Compare NVIDIA V100 32GB with similar GPUs from other brands.

GPU VRAM FP16 TFLOPS Bandwidth Hardware Price Cloud Price
V100 Current 32GB 31.4 900 GB/s $2.5k - -
AMD Instinct MI100 AMD 32GB (+0%) 184.6 (+488%) 1.2 TB/s - - Compare
AMD Radeon RX 7900 XTX AMD 24GB (-25%) 122.0 (+289%) 960 GB/s - - Compare
AMD Radeon RX 7900 XT AMD 20GB (-37%) 104.0 (+231%) 800 GB/s - - Compare
AMD Instinct MI210 AMD 64GB (+100%) 181.0 (+476%) 1.6 TB/s - - Compare

Best Use Cases

No specific use case recommendations for NVIDIA V100 32GB yet.

Browse All Use Cases →

Compare V100

Other NVIDIA GPUs

Frequently Asked Questions about V100

V100 32GB pricing depends on used-market condition, form factor, warranty, and availability. Compare the current hardware price on this page with rental alternatives before buying.

It is enough for many legacy training, inference, and research workloads, but newer LLMs may need quantization, smaller batches, or a larger-memory GPU.

For AI work, V100 32GB is usually more flexible because memory limits fail jobs before compute does. V100 16GB can still be fine for smaller models or legacy benchmarks.

The NVIDIA V100 32GB has a market price of approximately $2.5k. Cloud rental starts at $0.140/hr. Prices may vary based on retailer, region, and availability.

Yes, the NVIDIA V100 32GB with 32GB VRAM is suitable for many AI/ML workloads. For large language models, you may need multiple GPUs or consider higher-VRAM options like A100 or H100.

The breakeven point is approximately 17,857 hours of usage. Buy if you'll use it more than this; rent for shorter projects or variable workloads. Cloud rental from Datacrunch (Verda) starts at $0.140/hr.

With 32GB VRAM and 31.4 FP16 TFLOPS, the NVIDIA V100 32GB can run: Large language models (7B-13B), Stable Diffusion XL, video AI, and professional 3D rendering.

The NVIDIA V100 32GB offers 32GB VRAM and 31.4 FP16 performance at $2.5k. Compare with similar GPUs using our comparison tool above. Key factors: VRAM for model size, TFLOPS for speed, and price for budget.
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