RTX 5080 or RTX 4090 for local AI? The 4090, because 24GB runs the 27–32B models 16GB can't

RTX 5080 or RTX 4090 for local AI? For running language models, the RTX 4090. NVIDIA lists the 5080 with 16 GB of GDDR7 and the 4090 with 24 GB of GDDR6X, and that 8GB is the whole decision: the 27–32B models that are the sweet spot for local AI fit 24GB at Q4_K_M and don't fit 16GB at that quality. On the models both cards hold, they're close in speed. The 5080 is the right buy only if your models stay at 14B or below, or you want a cheaper, cooler, current card. Specs, speeds and prices below were read on November 6, 2026.

Which models fit on an RTX 5080 vs an RTX 4090?

Anything up to the 14B class fits both. From about 27B up, only the 4090 keeps Q4_K_M, the quantization level most people treat as the quality floor. We ran each model's published config through the same VRAM engine that powers our VRAM calculator on November 6, 2026. "Tight" means the model uses more than 90% of the card: it loads, but a browser or desktop on the same GPU can push it over.

Longer context widens the gap. At 32K context, Qwen3.8 27B drops to Q3_K_M on 16GB (about 15.4GiB) but keeps Q4_K_M on 24GB (about 18.5GiB). Qwen3-32B at 32K needs about 27.3GiB at Q4_K_M, which is too much for either card; the 4090 has to drop to Q3_K_M and the 5080 can't hold it at all. Gemma 4 and Qwen3.8 use hybrid attention, so add a few hundred MB for their fixed per-layer state. These are estimates from each model's config, not measurements. Per-model detail: best GPU for Qwen3.8 27B, Gemma 4 26B-A4B and Qwen3-32B.

hardware-corner reaches the same line from the other side. Its RTX 5080 summary says "the 16GB VRAM limit restricts users to models under 28B parameters", while its RTX 4090 page lists Qwen3 32B (Q4_K) as the biggest model that fits, at 34.4 tokens/sec with 16K context.

This is the same trap as the RTX 5060 Ti vs used RTX 3090 decision, one tier up. A newer 16GB card is still a 16GB card: the model file either fits in VRAM or it doesn't, and speed only matters between cards that can both load it.

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

Not by much either way. On models both cards hold, hardware-corner's llama.cpp tables (read November 6, 2026) put the 4090 slightly ahead at short and medium context, and the 5080 ahead on gpt-oss-20b in its native MXFP4 format at very long context. Token generation is bound by memory bandwidth, and NVIDIA lists 960 GB/sec for the 5080; hardware-corner lists 1,008 GB/s for the 4090.

These are hardware-corner's measurements, not benchmarks we ran. Its 4090 page says "Updated Benchmarks: March 2026"; its 5080 table names llama.cpp build 8233. One oddity worth knowing: both pages' headline cards disagree with their own tables (the 4090 card shows 78.7 t/s for 14B, its table 69.8 at 16K; the 5080 card labels 64.0 as 4K context, its table puts 64.0 at 16K and 80.6 at 4K), so we quote the tables.

RTX 5080 vs RTX 4090: the specs that matter for AI

NVIDIA lists 1,801 AI TOPS for the 5080 and 1,321 for the 4090. Don't read that as a speed ratio for local LLMs: generation speed tracks memory bandwidth, which is about equal here, and the table above shows it.

When is the RTX 5080 the better buy?

When your models stay at 14B or below, or the card also has to game. Then the 5080 does the job for about $800 less on hardware-corner's US averages, on 90W less power per NVIDIA, as a current-generation card. Specifically:

• Your models are 14B or smaller, or gpt-oss-20b. Both fit 16GB, and hardware-corner measured the 5080 within about 15% of the 4090 on them (64.0 vs 69.8 t/s on Qwen3-14B at 16K), ahead at 128K context on gpt-oss-20b.
• Price. About $1,400 vs $2,200 is a real gap. If the extra $800 doesn't buy you a model you'll actually run, it's wasted.
• Power and heat. 360W vs 450W total graphics power, per NVIDIA. Both list an 850W power supply.
• New vs used. The 4090 is a 2022 card and the 5090 has replaced it at the top of NVIDIA's range, so in late 2026 buying one often means the used market, without the warranty a new 5080 carries.
• FP4. NVIDIA's 5080 page pitches "Max AI performance with FP4". MXFP4 models like gpt-oss run natively on Blackwell; Ada has no FP4 support.

We haven't found a like-for-like image-generation benchmark of these two cards that we can source, so we don't call a winner for Stable Diffusion or Flux here. For image work, 16GB covers most common workflows; the VRAM argument above is an LLM argument first.

Can I try a 24GB card before I buy?

Yes, by the hour. Rent an RTX 4090, load the exact 27–32B model you want, and see whether you'd use it daily. In our price feed at 21:20 UTC on November 6, 2026, Vast.ai listed an RTX 4090 from $0.27 an hour on-demand (38 offers; $0.20 spot, which can be interrupted) and an RTX 5080 from $0.20 on-demand. Runpod listed its RTX 4090 at $0.89 and RTX 5080 at $0.59 on-demand. Prices move hourly; today's are on our cloud GPU price page. If you never reach past 14B, buy the 5080 with confidence. If you do, you've just shown yourself you need the 24GB.

Our call

Size the card to your real model list, not the newest badge. The VRAM calculator shows what fits in 16 vs 24GB, what it costs to run Qwen3-32B compares renting with buying, and our RTX 4090 vs RTX 3090 and is the RTX 5090 worth it pages cover the tiers around this one. For local AI: buy the memory first, then worry about speed.