RTX 4090 vs RTX 5090 for local AI
This is one of the hardware comparisons on this site, and the short version is that it depends on whether the extra memory moves a model class you actually care about across the line from "does not fit" to "fits". Both the RTX 4090 and the RTX 5090 are top-of-stack consumer cards that will run essentially everything in the local AI world, so the decision is about capacity and headroom rather than capability. Hardware covers working out what class of machine you need.
What fits
The 4090 carries 24 GB of GDDR6X. The 5090 carries 32 GB of GDDR7. That 8 GB gap is the single most important difference, because VRAM capacity is a hard wall: a model either fits or it spills to system RAM and slows to a crawl.
What the gap buys you, approximately, and always subject to quantization choices:
- Mid-size text models fit comfortably on both cards at common quantization levels, so there is no practical difference in that range.
- Larger models that sit just above 24 GB at a usable quantization become workable on the 5090 without dropping to a more aggressive, lower-quality quantization.
- Long context windows need room for the KV cache, which grows with context length and is often what pushes a model over the edge. The extra capacity is frequently spent here rather than on a bigger parameter count.
- Image and video work benefits at the high end: larger latent resolutions, longer video clips and keeping a model plus its adapters resident at once.
If your workloads already fit in 24 GB, the capacity argument disappears and you are buying speed.
Speed
Both cards sit in the high-bandwidth desktop class, and memory bandwidth is what governs token generation speed for local inference. GDDR7 on the 5090 is a generation ahead of the 4090's GDDR6X, and the newer Blackwell architecture also widens the bus, so the 5090 is clearly the faster card for bandwidth-bound generation.
Compute-bound work behaves differently. Prompt processing, image sampling and fine-tuning lean on raw arithmetic throughput and on newer low-precision data types that the later architecture supports natively. The newer generation wins there too, but the margin depends on whether the runtime underneath your app has kernels that use those data types yet.
Power is a real consideration. The 5090 pulls meaningfully more than the 4090 under sustained load. Both are large multi-slot cards on a 16-pin connector, so plan for PSU headroom, case airflow and, in a multi-GPU build, physical slot spacing.
Ecosystem
There is no ecosystem split here. Both cards are CUDA parts, and every mainstream app supports them: llama.cpp, Ollama and LM Studio all run on either with the same workflow, as do the common image and video stacks.
The one caveat is timing. Brand-new architectures need a newer CUDA toolchain, and prebuilt wheels, container images and hand-tuned kernels catch up over a period after launch rather than on day one. A card that has been in the wild longer tends to have smoother packaging. This gap closes; it is a reason to check the support notes for the app you plan to use, not a reason to avoid the newer card.
Who should pick which
- You already own a 4090. Stay put unless you have a specific model or context length that does not fit in 24 GB. The upgrade buys speed you will notice and capacity you may not need.
- You are buying new and memory-limited work is the point. Take the 5090. Large models, long contexts and high-resolution generation all cash in the extra 8 GB directly.
- You are building multi-GPU. Think in total capacity and in watts. Two 24 GB cards give you more aggregate memory than one 32 GB card, at the cost of a harder power and cooling problem and an engine that has to split the model across devices.