TL;DR: The RTX 5090 is genuinely faster for local AI inference — roughly 67% more tokens per second than the 4090 on the same models. But it costs ~$400 more (at MSRP), is nearly impossible to find at MSRP, and the 4090 is still excellent. For most users, the 4090 is the better buy today.
Bottom line: Buy the RTX 4090 if you can find one under $1,700. Only get the 5090 if you need maximum throughput and can pay a premium without flinching.
The Numbers
RTX 4090:
- 24GB GDDR6X
- 1,008 GB/s memory bandwidth
- ~127 t/s on Llama 3.1 8B Q4_K_M
- ~$1,600 (used or retail, as of February 2026)
RTX 5090:
- 32GB GDDR7
- 1,790 GB/s memory bandwidth
- ~213 t/s on Llama 3.1 8B Q4_K_M
- ~$2,000 (GPU only, at MSRP — if you can find it)
The bandwidth jump explains the performance gap. Memory bandwidth — how fast data moves between the GPU's memory and its processors — is the primary bottleneck for LLM inference, not raw compute. The 5090's 1,790 GB/s vs the 4090's 1,008 GB/s is a 77% increase in bandwidth, which translates almost directly to faster tokens per second.
What the Extra Speed Actually Means
At 127 t/s (4090), a 500-word response generates in roughly 8–10 seconds. At 213 t/s (5090), same response in 5–6 seconds. Real, noticeable difference if you're running it constantly. Less meaningful if you're a casual user running a few queries a day.
Where the 5090 pulls further ahead: larger models. The extra 8GB VRAM (32GB vs 24GB) lets you run models the 4090 can't — specifically, 70B models at Q2/Q3 start becoming more practical, and some 34B models that required heavy quantization on the 4090 can run at full Q4 quality.
Not sure what VRAM you need for your target models? See the VRAM breakdown by model size.
The Availability Problem
Here's the honest reality as of February 2026: RTX 5090 cards are extremely difficult to find at MSRP. Scalper prices are running $2,500–$3,500. If you're paying scalper prices, the math falls apart completely — the 4090 at $1,600 is a massively better value.
If you can get a 5090 at or near MSRP ($2,000), the conversation is different. $400 more for 67% more throughput and 8GB extra VRAM is a legitimate trade-off for power users. The pricing pressure isn't limited to flagships either — the RTX 5060 Ti is already climbing above MSRP.
Who Should Upgrade
Upgrade to 5090 if:
- You're building a new system and can get 5090 at/near MSRP
- You run large models (34B–70B) regularly and need better speed
- You're doing production-level inference and every second matters
Stick with 4090 if:
- You already own one — not worth selling and buying for 67% speed gain
- You can't find a 5090 at MSRP
- You're running 7B–13B models primarily (the 4090 is more than sufficient)
- Budget is a consideration — and if budget is the main driver, here's the cheapest way to run Llama 3 locally
Consider a Mac instead if:
- You need to run 70B+ models without dual-GPU complexity — see how the M4 Max handles models the 4090 can't load
Wait if:
- You're buying new and the 5090 situation hasn't normalized in your market — prices may settle in 3–6 months
If you're considering a more affordable 16GB card instead of these flagships, see how the RTX 5060 Ti, 4060 Ti, and Arc B580 compare.
For a broader look at the full GPU landscape across all price points, see the complete GPU comparison guide.