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

The RTX 4090 vs RTX 5090 question comes up whenever a workstation workflow moves to a server. One fact first: FullGPU 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.

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 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 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 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 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.

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.