# RTX 5090 vs RTX 4090 for AI: what 8 GB more memory buys you

> RTX 5090 vs RTX 4090 for AI: 32 GB vs 24 GB, 1.79 TB/s vs 1,008 GB/s and FP4. Which LLMs and image models fit, and what renting a 5090 costs per month.

Source: https://overgpu.com/compare/rtx-4090-vs-rtx-5090  
Updated: 2026-10-22  
Publisher: OverGPU (https://overgpu.com/)

**In short:** We rent the RTX 5090, not the 4090, and its extra 8 GB, higher bandwidth and FP4 support make it the better AI card; a 4090 you already own still suits jobs that fit in 24 GB.

## RTX 4090 vs RTX 5090 specifications

| Specification | NVIDIA GeForce RTX 4090 | NVIDIA GeForce RTX 5090 |
| --- | --- | --- |
| Architecture | Ada Lovelace | Blackwell |
| GPU memory | 24 GB GDDR6X | 32 GB GDDR7 |
| Memory bandwidth | 1.01 TB/s | 1.79 TB/s |
| FP4 | — | 3,352 AI TOPS |
| FP8 | 1,321 AI TOPS | Supported |
| BF16 / FP16 | Supported | Supported |
| GPU interconnect | PCIe Gen4 x16 | PCIe Gen5 x16 |
| Form factor | PCIe card · GeForce | PCIe card · GeForce |
| Max power | 450 W | 575 W |

## Monthly prices at OverGPU

| Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [NVIDIA GeForce RTX 5090](https://overgpu.com/gpus/nvidia-rtx-5090) | Single GPU | 32 GB GDDR7 | 16 vCPU | 64 GB | 1 TB NVMe | 10 Gbps | $449 | $0.62 | In stock, delivered in under 10 minutes after payment |

Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price.

## Choose the RTX 4090 if…

- You already own one and your work fits comfortably in 24 GB.
- You develop on a local workstation and rent GPUs only for bigger jobs.
- Your models run in FP8 or 4-bit with short contexts, so 24 GB is not a limit.

## Choose the RTX 5090 if…

- Your work needs more than 24 GB, such as a 12B image model in BF16 or long contexts.
- You want FP4 tensor-core support on Blackwell.
- Your serving is bandwidth-bound, where 1.79 TB/s beats 1,008 GB/s.
- You want to rent: we offer the RTX 5090 at $449 a month, not the RTX 4090.

## Overview

The RTX 4090 vs RTX 5090 question comes up whenever a workstation workflow moves to a server. One fact first: OverGPU does not rent the RTX 4090. We rent the GeForce RTX 5090, at $449 a month. The 4090 has 24 GB of GDDR6X at 1,008 GB/s; the 5090 has 32 GB of GDDR7 at 1.79 TB/s and adds FP4 support from the Blackwell architecture. Here is what those differences mean for real workloads.

## RTX 4090 vs RTX 5090: the short answer

For AI work, the RTX 5090 is the stronger card on every specification that matters: a third more memory, about 78% more memory bandwidth and FP4 tensor-core support. The RTX 4090 remains a capable card for anything that fits in 24 GB, and if you own one, keep using it for that. We chose the 5090 for our catalog because its extra 8 GB moves several common workloads from "needs workarounds" to "fits".

## What the extra 8 GB enables

Weights take roughly 2 bytes per parameter in BF16, 1 in FP8 and 0.5 in 4-bit formats, plus 10–30% for KV cache, activations and overhead. At 24 GB, many popular model sizes sit right on the edge; at 32 GB, they fit with room left.

| Workload | Memory, roughly | RTX 4090, 24 GB | RTX 5090, 32 GB |
| --- | --- | --- | --- |
| SDXL-class image generation | Under 16 GB | Fits | Fits |
| 12B image model (Flux-class), BF16 | About 24 GB plus text encoders | Needs FP8 weights or offloading | Fits, with text encoders offloaded |
| 14B LLM, FP8 | 14 GB + KV cache | Fits, about 10 GB spare | Fits, about 18 GB spare |
| 32B LLM, 4-bit | About 16 GB + KV cache | Tight, about 8 GB spare | Fits, about 16 GB spare |
| 8B LLM in FP8 with a 128k-token context | About 8 GB + 17 GB KV cache | Does not fit | Fits |
| 14B LLM, BF16 | 28 GB + KV cache | Does not fit | Tight |

The long-context row is where 8 GB matters most. Llama 3.1 8B uses grouped-query attention with 32 layers and 8 KV heads, so its KV cache takes about 0.13 MB per token in 16-bit, or about 17 GB for a full 128k-token context. Add about 8 GB of FP8 weights and you need roughly 25 GB before overhead: over the 4090's limit, within the 5090's.

For image generation, the extra memory lets a 12B model's transformer stay in BF16 instead of being squeezed into FP8, and leaves room for larger batches or more components loaded at once. For SDXL-class models, both cards have plenty. Our RTX 5090 plan also has 64 GB of system RAM, enough to hold offloaded text encoders or idle pipeline components while the GPU works. The [VRAM calculator](https://overgpu.com/tools/vram-calculator) runs the same arithmetic for your model.

Fine-tuning follows the same pattern. Full fine-tuning with Adam needs roughly 16–20 bytes per parameter before activations, which caps it at about 1.2–1.5B parameters on 24 GB and 1.6–2B on 32 GB. Adapter methods go much further. LoRA on an 8B model, with about 16 GB of frozen BF16 weights, fits on both cards, with more room for sequence length on the 5090. QLoRA on a 32B model, about 16 GB of 4-bit weights plus adapters and activations, is tight on 24 GB and workable on 32 GB.

## Memory bandwidth: 1,008 GB/s vs 1.79 TB/s

NVIDIA lists 1,008 GB/s for the RTX 4090's GDDR6X and 1.79 TB/s for the RTX 5090's GDDR7, about 78% more. Each generated token requires reading the model's weights and KV cache from memory, so for LLM serving at small batch sizes, bandwidth is usually the limit. On a model that fits both cards, this is the most relevant hardware difference for token generation. At larger batch sizes, serving shifts toward compute, which is where FP4 support starts to matter. Diffusion models lean more on compute too, so there the gain depends on precision and kernels.

## FP4 and precision formats

The RTX 4090 is an Ada Lovelace card: its tensor cores go down to FP8, and NVIDIA quotes 1,321 AI TOPS for FP8 with sparsity. The RTX 5090 is Blackwell and adds FP4; NVIDIA quotes 3,352 AI TOPS for FP4 with sparsity. The two headline numbers use different precisions, so they are not like for like: part of the jump comes from halving the bits.

FP4 pays off when your model keeps its accuracy at 4 bits and your framework has FP4 kernels for Blackwell. You can still run 4-bit weights on a 4090 with weight-only quantization such as AWQ or GPTQ, but the math runs at higher precision, so memory use drops without a compute gain. Our [FP8 and FP4 explainer](https://overgpu.com/blog/fp8-fp4-precision-explained) covers the accuracy trade-offs.

## Power, PCIe and software

The RTX 5090 is rated at 575 W against 450 W for the 4090. On a rented server, that is our concern rather than a line on your invoice, since the monthly price is flat. The 5090 also moves to a PCIe Gen5 interface, twice the per-lane rate of the 4090's Gen4, which matters for workloads that move a lot of data between system RAM and the GPU.

One practical note when moving from a 4090: Blackwell needs CUDA 12.8 or later and a matching driver, so containers and PyTorch builds made for older CUDA versions may need rebuilding. Our Ubuntu images come with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled; see [NVIDIA drivers and CUDA](https://overgpu.com/docs/nvidia-drivers-cuda) for details.

## Cost: the RTX 5090 at $449 a month

Our RTX 5090 plan is $449 a month, about $0.62 per GPU-hour, with 16 vCPU, 64 GB of RAM, 1 TB of NVMe storage and 20 TB of outbound bandwidth a month under fair use. It is our lowest-priced GPU and the entry point to Blackwell. The price also covers root access over SSH, a public IPv4 address and an IPv6 /64, DDoS mitigation and 24/7 support, with no setup fee. If you run around the clock, a flat monthly price is easier to plan than an hourly meter; our [monthly vs hourly GPU pricing](https://overgpu.com/compare/monthly-vs-hourly-gpu-pricing) comparison shows when each model makes sense. If your work outgrows 32 GB, the next step is the 96 GB RTX PRO 6000 at $1,390 a month; see [RTX 5090 vs RTX PRO 6000](https://overgpu.com/compare/rtx-5090-vs-rtx-pro-6000).

## How to decide

- You own a 4090 and your work fits in 24 GB: keep using it.
- Your work hits the 24 GB ceiling, with FP8 workarounds, offloading or short contexts: the 5090's 32 GB removes it for many models.
- You want to rent: the RTX 5090 is the card we offer, with a one-month minimum term and no cancellation fee.
- You need more than 32 GB: skip both and look at the RTX PRO 6000.

The two cards also work well together. Develop and test on the 4090 you own, then move the jobs that need 32 GB, or need to run around the clock, to a rented 5090.

## Frequently asked questions

### Does OverGPU rent the RTX 4090?

No. We rent the GeForce RTX 5090 at $449 a month, with 32 GB of GDDR7 and FP4 support, and the 96 GB RTX PRO 6000 at $1,390 a month when you need more memory.

### How much faster is the RTX 5090 than the RTX 4090?

We do not publish benchmarks. On vendor figures, the 5090 has about 78% more memory bandwidth and adds FP4, while NVIDIA's headline AI TOPS use FP4 for the 5090 and FP8 for the 4090, so they are not directly comparable.

### Is 24 GB enough for Flux?

Only with compromises: the 12B Flux-class transformer takes about 24 GB in BF16, so a 24 GB card needs FP8 weights or offloading. On the RTX 5090's 32 GB, the BF16 transformer fits with the text encoders offloaded.

### What is the largest LLM the RTX 5090 can serve?

Roughly a 32B model in 4-bit, about 16 GB of weights, with room left for KV cache. A 70B model needs about 35 GB even in 4-bit, more than the 5090's 32 GB.

### Will my RTX 4090 code run on the RTX 5090?

Generally yes, since both use CUDA, but Blackwell needs CUDA 12.8 or later. Rebuild containers or reinstall PyTorch with a CUDA version that supports Blackwell.

## Related pages

- [Rent a dedicated RTX 5090 cloud server for $449 per month](https://overgpu.com/gpus/nvidia-rtx-5090): Rent a whole RTX 5090 (32 GB GDDR7) in a dedicated VM for $449/month, about $0.62 per GPU-hour at 24/7. For image generation, small LLMs and dev work.
- [RTX 5090 vs RTX PRO 6000: when is 32 GB enough?](https://overgpu.com/compare/rtx-5090-vs-rtx-pro-6000): A 32 GB card at $449 a month or a 96 GB pro card with ECC and MIG at $1,390. See exactly when 32 GB is enough for your work.
- [RTX PRO 6000 vs L40S: is 96 GB worth the step up?](https://overgpu.com/compare/rtx-pro-6000-vs-l40s): 96 GB vs 48 GB, FP4 vs FP8, $1,390 vs $890 a month. See which card your work needs.
- [LLM VRAM calculator: will your model fit on a GPU?](https://overgpu.com/tools/vram-calculator): Pick Llama, Qwen, Mistral or gpt-oss, set the precision and context length, and see weights, KV cache and which GPUs fit.
- [How much VRAM do I need for an LLM?](https://overgpu.com/blog/vram-for-llms): Quick answers for 7B, 14B, 32B, 70B and 405B models at BF16, FP8 and 4-bit, plus KV cache by context and the GPU that fits.
- [A dedicated GPU for image and video generation](https://overgpu.com/solutions/generative-media): Approximate VRAM for Stable Diffusion, Flux and video models, on dedicated GPUs. Compare the cards.