https://serverrental.store/index.php?action=history&feed=atom&title=NVIDIA_Tesla_T4_ServerNVIDIA Tesla T4 Server - Revision history2026-04-14T21:48:09ZRevision history for this page on the wikiMediaWiki 1.36.1https://serverrental.store/index.php?title=NVIDIA_Tesla_T4_Server&diff=5710&oldid=prevAdmin: New server config article2026-04-12T15:43:36Z<p>New server config article</p>
<p><b>New page</b></p><div>'''NVIDIA Tesla T4 Server''' is the most affordable data center GPU cloud server available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud]. At just $0.23/hr, the Tesla T4 is optimized for inference workloads with its low power consumption and INT8/FP16 Tensor Cores.<br />
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== Specifications ==<br />
{| class="wikitable"<br />
|-<br />
! Component !! Specification<br />
|-<br />
| '''GPU''' || NVIDIA Tesla T4 (Turing architecture)<br />
|-<br />
| '''VRAM''' || 16 GB GDDR6<br />
|-<br />
| '''CUDA Cores''' || 2,560<br />
|-<br />
| '''Memory Bandwidth''' || 320 GB/s<br />
|-<br />
| '''INT8 Performance''' || 130 TOPS<br />
|-<br />
| '''FP16 Performance''' || 65 TFLOPS<br />
|-<br />
| '''TDP''' || 70W<br />
|-<br />
| '''Starting Price''' || From $0.23/hr<br />
|}<br />
<br />
== Performance ==<br />
The Tesla T4 was designed from the ground up for inference, not training:<br />
* '''70W TDP''' — lowest power consumption of any data center GPU<br />
* '''130 TOPS INT8''' — excellent for quantized inference<br />
* '''16 GB GDDR6''' — sufficient for most inference models<br />
* '''Turing Tensor Cores''' — FP16, INT8, INT4 acceleration<br />
<br />
The T4 is not suitable for training large models — its 2,560 CUDA cores and 320 GB/s bandwidth are far below training-oriented GPUs. However, for inference it punches well above its price:<br />
* Runs BERT-class models at high throughput<br />
* Handles computer vision inference efficiently<br />
* Supports TensorRT optimization for maximum inference speed<br />
* INT8 quantization achieves near-FP16 accuracy at 2x throughput<br />
<br />
== Best Use Cases ==<br />
* Production inference serving (highest cost efficiency)<br />
* API endpoints for ML models<br />
* Real-time NLP inference (sentiment analysis, text classification)<br />
* Computer vision inference (object detection, OCR)<br />
* Edge-like inference at data center reliability<br />
* Batch inference processing<br />
* ML model serving with TensorRT optimization<br />
<br />
== Pros and Cons ==<br />
=== Advantages ===<br />
* $0.23/hr — cheapest data center GPU available<br />
* 70W TDP — extremely power efficient<br />
* ECC GDDR6 for data integrity<br />
* 130 TOPS INT8 — excellent inference throughput<br />
* 16 GB VRAM handles most inference models<br />
* Data center-grade reliability<br />
<br />
=== Limitations ===<br />
* Not suitable for model training (too slow)<br />
* Only 2,560 CUDA cores<br />
* 320 GB/s memory bandwidth is limited<br />
* Older Turing architecture<br />
* No NVLink support<br />
* FP32 performance is poor<br />
<br />
== Pricing ==<br />
Available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud] starting at '''$0.23/hr''' — the lowest price in the entire GPU lineup. Monthly cost for 24/7: approximately $166. Unbeatable for always-on inference.<br />
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== Recommendation ==<br />
The '''NVIDIA Tesla T4 Server''' is the ultimate budget inference GPU. If you're deploying ML models to production and need the lowest possible per-query cost, the T4 with TensorRT optimization is the clear winner. Do NOT use this for training — even a [[NVIDIA RTX 3080 Server]] at $0.48/hr will train 5–10x faster. For inference with more VRAM, see the [[NVIDIA Tesla A2 Server]] or [[NVIDIA Tesla A10 Server]].<br />
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== See Also ==<br />
* [[NVIDIA Tesla A2 Server]]<br />
* [[NVIDIA Tesla A10 Server]]<br />
* [[NVIDIA V100 Server]]<br />
<br />
[[Category:GPU Servers]]<br />
[[Category:Data Center GPU]]<br />
[[Category:Budget GPU]]<br />
[[Category:Inference GPU]]</div>Admin