https://serverrental.store/index.php?action=history&feed=atom&title=NVIDIA_V100_ServerNVIDIA V100 Server - Revision history2026-04-14T21:48:07ZRevision history for this page on the wikiMediaWiki 1.36.1https://serverrental.store/index.php?title=NVIDIA_V100_Server&diff=5704&oldid=prevAdmin: New server config article2026-04-12T15:39:32Z<p>New server config article</p>
<p><b>New page</b></p><div>'''NVIDIA V100 Server''' is a budget data center GPU cloud server available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud]. The V100 was NVIDIA's first Tensor Core GPU and remains viable for many ML workloads at a fraction of the cost of newer GPUs.<br />
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== Specifications ==<br />
{| class="wikitable"<br />
|-<br />
! Component !! Specification<br />
|-<br />
| '''GPU''' || NVIDIA Tesla V100 (Volta architecture)<br />
|-<br />
| '''VRAM''' || 32 GB HBM2<br />
|-<br />
| '''Memory Bandwidth''' || 900 GB/s<br />
|-<br />
| '''FP16 Performance''' || ~125 TFLOPS<br />
|-<br />
| '''FP32 Performance''' || ~15.7 TFLOPS<br />
|-<br />
| '''Interconnect''' || NVLink 2.0 (300 GB/s)<br />
|-<br />
| '''Starting Price''' || From $1.08/hr<br />
|}<br />
<br />
== Performance ==<br />
The V100 introduced Tensor Cores to the world and proved their value for deep learning. While two generations behind the H100, it still offers:<br />
* '''32 GB HBM2''' — sufficient for models up to ~13B parameters with quantization<br />
* '''1st-gen Tensor Cores''' with FP16 mixed precision<br />
* '''900 GB/s memory bandwidth''' — adequate for most inference workloads<br />
<br />
Performance comparison:<br />
* Roughly 3x slower than A100 for FP16 training<br />
* 5–6x slower than H100 for transformer training<br />
* Still faster than any consumer GPU for sustained compute workloads<br />
* Excellent for inference of small-to-medium models<br />
<br />
At $1.08/hr, the V100 costs '''55% less than the A100''' and '''72% less than the H100''', making it attractive for budget-conscious ML work.<br />
<br />
== Best Use Cases ==<br />
* Budget ML training for smaller models (up to 7B with quantization)<br />
* Inference serving for production models<br />
* ML experimentation and prototyping<br />
* Educational and learning environments<br />
* Classical ML workloads (XGBoost GPU, Random Forests)<br />
* Computer vision inference (YOLO, ResNet, EfficientNet)<br />
* NLP inference for BERT-class models<br />
<br />
== Pros and Cons ==<br />
=== Advantages ===<br />
* Very affordable at $1.08/hr<br />
* 32 GB HBM2 — more VRAM than consumer GPUs<br />
* Data center-grade reliability (ECC memory)<br />
* Tensor Cores for accelerated ML<br />
* Well-supported across all major frameworks<br />
<br />
=== Limitations ===<br />
* Only 32 GB VRAM limits model size<br />
* Two generations behind current (Volta vs Hopper)<br />
* No TF32, BF16, FP8, or INT8 Tensor Core support<br />
* Lower memory bandwidth than A100/H100<br />
* No Multi-Instance GPU support<br />
<br />
== Pricing ==<br />
Available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud] starting at '''$1.08/hr'''. Monthly cost for 24/7: approximately $778. An excellent entry point for data center GPU compute.<br />
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== Recommendation ==<br />
The '''NVIDIA V100 Server''' is the budget data center GPU choice. It's perfect for startups and researchers who need real Tensor Core performance but can't justify A100/H100 pricing. Ideal for inference, small model training, and prototyping. When you outgrow the V100's 32 GB VRAM or need newer precision formats, upgrade to the [[NVIDIA A100 Server]].<br />
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== See Also ==<br />
* [[NVIDIA A100 Server]]<br />
* [[NVIDIA Tesla T4 Server]]<br />
* [[NVIDIA RTX 3090 Server]]<br />
<br />
[[Category:GPU Servers]]<br />
[[Category:AI Training]]<br />
[[Category:Data Center GPU]]<br />
[[Category:Budget GPU]]</div>Admin