https://serverrental.store/index.php?action=history&feed=atom&title=NVIDIA_RTX_4090_ServerNVIDIA RTX 4090 Server - Revision history2026-04-14T21:47:44ZRevision history for this page on the wikiMediaWiki 1.36.1https://serverrental.store/index.php?title=NVIDIA_RTX_4090_Server&diff=5706&oldid=prevAdmin: New server config article2026-04-12T15:42:06Z<p>New server config article</p>
<p><b>New page</b></p><div>'''NVIDIA RTX 4090 Server''' is a popular consumer GPU cloud server available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud]. The RTX 4090 is the most widely-used consumer GPU for ML workloads, offering an excellent balance of 24 GB VRAM, 16,384 CUDA cores, and Ada Lovelace architecture.<br />
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== Specifications ==<br />
{| class="wikitable"<br />
|-<br />
! Component !! Specification<br />
|-<br />
| '''GPU''' || NVIDIA GeForce RTX 4090 (Ada Lovelace architecture)<br />
|-<br />
| '''VRAM''' || 24 GB GDDR6X<br />
|-<br />
| '''CUDA Cores''' || 16,384<br />
|-<br />
| '''Memory Bandwidth''' || 1,008 GB/s<br />
|-<br />
| '''Tensor Cores''' || 4th Generation (FP8 support)<br />
|-<br />
| '''TDP''' || 450W<br />
|-<br />
| '''Starting Price''' || From $0.93/hr<br />
|}<br />
<br />
== Performance ==<br />
The RTX 4090 has become the de facto standard for cost-effective ML compute:<br />
* '''16,384 CUDA cores''' with Ada Lovelace architecture<br />
* '''4th-gen Tensor Cores''' supporting FP8, FP16, BF16, TF32<br />
* '''24 GB GDDR6X''' — sufficient for most models up to 13B parameters<br />
* '''1,008 GB/s bandwidth''' — competitive with previous-gen data center GPUs<br />
<br />
Performance comparisons:<br />
* ~80% of A100 performance for FP16 training at 61% less cost<br />
* ~2x faster than RTX 3090 across most ML benchmarks<br />
* Can run 7B LLMs at good speed with 4-bit quantization<br />
* Excellent for Stable Diffusion / AI image generation (fastest consumer option)<br />
<br />
At $0.93/hr, the RTX 4090 is the most popular GPU for independent ML researchers and small teams.<br />
<br />
== Best Use Cases ==<br />
* ML model training and fine-tuning (up to 7B–13B parameters)<br />
* AI image generation (Stable Diffusion, Midjourney-style)<br />
* LLM inference with quantization (GPTQ, AWQ, GGUF)<br />
* Computer vision training and inference<br />
* 3D rendering and real-time ray tracing<br />
* Video AI processing<br />
* Kaggle competitions and ML experimentation<br />
<br />
== Pros and Cons ==<br />
=== Advantages ===<br />
* $0.93/hr — best performance per dollar for ML<br />
* 24 GB VRAM handles most practical models<br />
* FP8 Tensor Cores (same generation as H100)<br />
* Massive community support and optimization<br />
* Wide framework and model compatibility<br />
<br />
=== Limitations ===<br />
* 24 GB VRAM limits larger model training<br />
* No ECC memory<br />
* No NVLink support for multi-GPU training<br />
* Consumer-grade reliability<br />
* GDDR6X bandwidth lower than HBM on data center GPUs<br />
<br />
== Pricing ==<br />
Available from [https://en.immers.cloud/signup/r/20241007-8310688-334/ Immers Cloud] starting at '''$0.93/hr'''. Monthly cost for 24/7: approximately $670. One of the most cost-effective GPU options available.<br />
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== Recommendation ==<br />
The '''NVIDIA RTX 4090 Server''' is the top recommendation for most individual ML practitioners and small teams. At under $1/hr with 24 GB VRAM and Ada Lovelace Tensor Cores, it offers unbeatable value. Start here for fine-tuning, inference, and image generation. Only upgrade to data center GPUs ([[NVIDIA A100 Server|A100]], [[NVIDIA H100 Server|H100]]) when you need more VRAM, ECC, or multi-GPU NVLink.<br />
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== See Also ==<br />
* [[NVIDIA RTX 5090 Server]]<br />
* [[NVIDIA RTX 3090 Server]]<br />
* [[NVIDIA A100 Server]]<br />
* [[NVIDIA RTX A5000 Server]]<br />
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[[Category:GPU Servers]]<br />
[[Category:Consumer GPU]]<br />
[[Category:AI Training]]</div>Admin