RTX 5080 vs RTX 4090: Which GPU Is Better for AI Workloads?

On inference speed, the RTX 5080 and RTX 4090 are close to a tie: same-lab tests split narrowly either way — the 4090 leads on Qwen3-8B at 16K context (104.3 vs 94.1 tokens per second) and on every UL Procyon suite, while the 5080 edges the same-lab Llama-3.1-8B test (132 vs 127 tokens per second). The real difference is memory capacity: the RTX 4090 has 24 GB of GDDR6X and the RTX 5080 has 16 GB of GDDR7, per NVIDIA's specifications. Quantized 27B and 32B class models need 20 to 23 GB, which fits the 4090 and does not fit the 5080. Best for 24 GB-class models: RTX 4090. Best for 8B-to-14B models, lower power, and a new-card warranty: RTX 5080. Specs checked August 21, 2026.

How do the specifications compare?

The RTX 4090 is a 2022 flagship with more memory and more bandwidth; the RTX 5080 is a 2025 card that gets within reach of its throughput at two-thirds of the MSRP and 90 W less board power. The full specification comparison is below, per NVIDIA's specification pages for both cards.

Which GPU has more VRAM, and why does it matter for AI?

The RTX 4090 has 24 GB of VRAM; the RTX 5080 has 16 GB, per NVIDIA's specifications. VRAM capacity decides which AI models fit on the card without offloading layers to system memory, so those 8 GB are the single most important difference between these two GPUs for AI work — more important than any speed gap.

The table below shows the memory our VRAM calculator computes for popular models at Q4_K_M quantization, including KV cache and runtime overhead:

In practice, the RTX 4090 is the least expensive single NVIDIA GPU that holds a quantized 27B-to-32B model entirely in VRAM, and it is also the card of the two that runs FLUX.1-dev at BF16 precision: the tracked Spheron test needs memory-efficient attention to hold FLUX.1-dev BF16 within the 4090's 24 GB and produced 4 images per minute (an approximate figure) — a pipeline too large for the 5080's 16 GB. Neither card holds a 70B model at Q4 on its own: that workload needs partial offloading to system RAM (slower) or a second GPU.

Which GPU is faster for LLM inference?

On the evidence in our benchmark database, the two cards are close to a tie, and which card reads faster depends on the test. In the same-lab llama-bench ranking from Hardware Corner (Qwen3-8B at Q4_K_XL with 16K context), the RTX 4090 generates at 104.3 tokens per second and the RTX 5080 at 94.1. In LocalAI Master's same-settings Llama-3.1-8B Q4_K_M test, the RTX 5080 generates at 132 tokens per second and the RTX 4090 at 127. Independent cross-checks support both rows: MyAIHardware records the 4090 at 125 tokens per second on the same model and quantization, within two percent of LocalAI Master's 4090 figure.

Token generation on a single GPU is largely bound by memory bandwidth, and the two cards are nearly matched there too: 1008 GB/s for the 4090 versus 960 GB/s for the 5080, per NVIDIA's specifications. Prompt processing (ingesting input text) shows the same near-tie: 4800 tokens per second on the 4090 (MyAIHardware) versus approximately 5200 on the 5080 (LocalAI Master, reported as approximate) — different labs, disclosed as such.

Because the speed gap is small in both directions, VRAM capacity — not throughput — should drive the choice between these two cards for LLM work.

Which GPU is faster for image generation?

The RTX 4090 leads on every same-lab image generation test in our database. StorageReview tested both cards on the same ThreadRipper 7980X platform with UL Procyon AI Image Generation (TensorRT):

On SDXL Turbo at FP16, our database records 80 images per minute on the 4090 (Tom's Hardware) versus 65 images per minute on the 5080 (TechPowerUp) — different publications, so treat that pair as indicative rather than same-lab. Capacity matters as much as speed here: the 4090's 24 GB holds FLUX.1-dev at BF16 (about 4 images per minute in Spheron's approximate test) and larger batch queues, while the 5080's 16 GB confines comfortable work to SD 1.5 and SDXL class pipelines.

Which GPU costs less?

At launch MSRP, the RTX 5080 costs less: $999 versus $1,599 for the RTX 4090, per NVIDIA's announced pricing. The practical 2026 situation is different: the 4090 is out of volume retail production, so most 4090 offers are used or leftover stock, while the 5080 is a current retail card. MSRP is not street price — availability moves real prices in both directions, so check current price via the links below before deciding, and see our used RTX 4090 buying guide for what to inspect on the second-hand market.

• Check current RTX 5080 price on Amazon
• Check current RTX 4090 price on Amazon

We deliberately avoid computing performance-per-dollar ratios: the right denominator depends on your workload mix, and street prices change too often for derived value math to stay honest. The benchmark numbers above, attributed to their sources, let you judge value against whichever price you actually find.

How much power does each GPU need?

The RTX 4090 has a 450 W total board power rating; the RTX 5080 is rated at 360 W, per NVIDIA's specifications. That 90 W difference affects power supply sizing, case airflow, and how much heat the system dumps under sustained AI loads. Sustained LLM inference and image generation hold the GPU near its power limit for hours, so a power supply with comfortable headroom — 850 W class for the 4090, 750 W class for the 5080 — and cooling designed for sustained load, not just gaming bursts, matter more here than in a gaming build.

What are the disadvantages of each GPU?

Neither card is a free win: the 4090 draws more power and is mostly a used-market buy, while the 5080 gives up 8 GB of VRAM that gates entire model classes. The specific trade-offs follow.

RTX 4090 disadvantages:

• Higher power draw: 450 W versus 360 W total board power, per NVIDIA's specifications.
• Out of volume retail production — most units sell used, with the warranty and inspection risks that implies.
• PCIe 4.0 x16 interface versus PCIe 5.0 on the 5080 (a minor factor for single-GPU AI inference, but a spec disadvantage on paper).

RTX 5080 disadvantages:

• Less VRAM: 16 GB caps comfortable model sizes below the 27B class, per our VRAM calculator (Gemma 3 27B needs 20.2 GB at Q4_K_M), and rules out BF16 FLUX.1-dev pipelines.
• Lower memory bandwidth: 960 GB/s versus 1008 GB/s — a near-tie that still shows up as 94.1 versus 104.3 tokens per second in the same-lab Qwen3 test.
• Loses every same-lab UL Procyon AI Text and Image suite to the 4090 in our database.

Who should buy the RTX 4090, and who should buy the RTX 5080?

Buy the RTX 4090 if you run 27B-to-32B class models or FLUX.1-dev at BF16: its 24 GB of VRAM is the cheapest single-card entry into those workloads, and the near-tie on generation speed means you give up almost nothing per token. A carefully inspected used 4090 at the right price is one of the best values in local AI. Buy the RTX 5080 if your models fit in 16 GB: you get equivalent 8B-to-14B throughput, 90 W less board power, a current retail channel with warranty, and the Blackwell feature set at a $999 MSRP.

Best for 24 GB-class models and BF16 FLUX pipelines: RTX 4090. It is the only one of the two that holds a quantized 27B-to-32B model entirely in VRAM.

Best for cost-conscious 8B-to-14B model work: RTX 5080. At 132 tokens per second on Llama-3.1-8B in the same-lab test and 360 W board power, it matches or beats the 4090's speed for mainstream assistant-class models while costing less to buy and run.

To dig further, the full spec sheets and per-model fit lists for both cards are in our interactive comparison: RTX 4090 vs RTX 5080 interactive comparison.

How did we compare these two GPUs?

We compared the RTX 5080 and RTX 4090 using only two classes of evidence: manufacturer specifications and published benchmark results tracked in our benchmark database. Specifications (VRAM, memory type, bus width, bandwidth, board power, CUDA cores, PCIe interface, architecture, MSRP) come from NVIDIA's specification pages. Benchmark numbers come from Hardware Corner (llama-bench, Qwen3-8B Q4_K_XL at 16K context), LocalAI Master and MyAIHardware (llama.cpp Llama-3.1-8B Q4_K_M, cross-checked where the sources overlap and agreeing within two percent), StorageReview (UL Procyon AI Text and Image suites, TensorRT, same platform), Tom's Hardware and TechPowerUp (SDXL Turbo images per minute, different publications, disclosed), and Spheron (FLUX.1-dev BF16, approximate). Where a figure is approximate or flagged as an estimate in the source data, we say so. Model memory requirements come from our own VRAM calculator at Q4_K_M with 4K context. We publish no derived ratios and no street prices. Facts checked August 21, 2026.

Frequently Asked Questions

These are the questions buyers actually ask when choosing between the RTX 5080 and RTX 4090 for AI work.

Is the RTX 5080 faster than the RTX 4090 for AI?

Not meaningfully — the tracked results are close to a tie and split both ways. The 4090 leads the same-lab Qwen3-8B 16K test (104.3 versus 94.1 tokens per second) and every UL Procyon suite; the 5080 leads the same-lab Llama-3.1-8B Q4_K_M test (132 versus 127 tokens per second). Choose on VRAM capacity and price, not speed.

Can the RTX 5080 run Gemma 3 27B or Qwen 3 32B?

Not entirely in VRAM. Gemma 3 27B needs 20.2 GB and Qwen 3 32B needs 22.7 GB at Q4_K_M including KV cache and overhead, per our VRAM calculator — both exceed the 5080's 16 GB and fit the 4090's 24 GB.

Can the RTX 4090 run Llama 3.3 70B?

Not entirely in VRAM. Llama 3.3 70B at Q4_K_M requires about 47.9 GB including KV cache and overhead, per our VRAM calculator, versus the 4090's 24 GB. The one tracked attempt in our database reports 18 tokens per second with heavy swapping, and that figure is flagged as a rough estimate in the source data.

Is a used RTX 4090 worth it over a new RTX 5080?

If you need the 24 GB class of models, yes — the 4090 is the cheapest single-card way to run quantized 27B-to-32B LLMs and BF16 FLUX.1-dev, and its generation speed is within a few percent of the 5080. If your models fit in 16 GB, the new 5080's warranty, 90 W lower power draw, and $999 MSRP are the saner buy. Our used RTX 4090 buying guide covers what to inspect before buying second-hand.

Which GPU uses less power?

The RTX 5080: 360 W total board power versus 450 W for the RTX 4090, per NVIDIA's specifications. The 90 W difference compounds over hours of sustained inference or image generation.

Do both GPUs support PCIe 5.0?

No. The RTX 5080 uses PCIe 5.0 x16; the RTX 4090 uses PCIe 4.0 x16, per NVIDIA's specifications. For single-GPU AI inference, VRAM capacity and bandwidth matter far more than the interface generation.

Sources

Specification and benchmark claims in this article come from the following origins.

• NVIDIA GeForce RTX 5080 and GeForce RTX 4090 official specification pages (manufacturer specifications): https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5080/
• Hardware Corner LLM GPU Rankings — Qwen3-8B Q4_K_XL 16K llama-bench tokens per second: https://www.hardware-corner.net/gpu-ranking-local-llm/
• LocalAI Master GPU Benchmarks — Llama-3.1-8B Q4_K_M generation and prompt processing (same-settings 5080/4090/5090 measurements): https://localaimaster.com/blog/rtx-5090-vs-5080-local-ai
• MyAIHardware llama.cpp Benchmarks — Llama-3.1-8B Q4_K_M generation and prompt processing: https://www.myaihardware.com/llama-cpp-benchmarks/
• StorageReview — RTX 5080 review with UL Procyon AI Text and Image scores for both cards (TensorRT): https://www.storagereview.com/review/nvidia-geforce-rtx-5080-review-the-sweet-spot-for-ai-workloads
• Tom's Hardware GPU Benchmarks — SDXL Turbo images per minute (RTX 4090): https://www.tomshardware.com/pc-components/gpus
• TechPowerUp GPU Reviews — SDXL Turbo images per minute (RTX 5080): https://www.techpowerup.com/reviews/
• Spheron GPU Benchmark Blog — FLUX.1-dev BF16 images per minute (RTX 4090, approximate): https://www.spheron.network/blog/rtx-5090-vs-rtx-4090/

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