RTX 5090 vs 5080 for Local AI: Which GPU Should You Buy?

Short answer: the RTX 5080 (16GB GDDR7, 960 GB/s) is the better value for local LLMs up to 14B, where its bandwidth ceiling is about 195 tok/s on an 8B model. The RTX 5090 (32GB GDDR7, 1,792 GB/s) is the card for 30B-class models like Qwen 2.5 32B and DeepSeek R1 32B, which do not fit in 16GB; DatabaseMart's published Ollama run had a single 5090 at 45.5 tok/s on deepseek-r1:32b. Buy the 5080 unless you regularly run 30B+ parameter models.

Updated November 2, 2026: prices re-read. Both Founders Editions ($999 and $1,999 launch prices) were out of stock on NVIDIA's marketplace; the cheapest in-stock cards we found were a PNY RTX 5080 at $1,579 and a GIGABYTE AORUS RTX 5090 at $3,340 (Walmart). Speed figures are now either labelled bandwidth ceilings or published third-party runs with the source named. A section on dual RTX 5080 vs one RTX 5090 is new.

NVIDIA's Blackwell-generation RTX 5090 and RTX 5080 are the two most capable consumer GPUs for running AI locally. With a launch-price gap of $1,000 (closer to $1,800 at November street prices) and double the VRAM on the 5090, choosing between them comes down to which models you need to fit.

This guide gives the spec and bandwidth maths, the published Ollama runs that exist, model-fit tables and price-per-GB to help you make the right choice. For model picks per card, see the RTX 5080 page and the RTX 5090 page.

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Quick Verdict

RTX 5080 ($999 launch, $1,579 street on Nov 2, 2026, 16GB) — Best value for most local AI users. Runs all 7B-14B models fully on the GPU, handles Stable Diffusion XL comfortably, and has a bandwidth ceiling of about 195 tok/s on an 8B model. If your primary models are 14B or smaller, this is the smart buy.

RTX 5090 ($1,999 launch, $3,340 street on Nov 2, 2026, 32GB) — Required for 30B+ models. The 32GB VRAM fits Qwen 2.5 32B, DeepSeek R1 32B, and a Q2_K 70B entirely in VRAM. It has the most memory bandwidth of any consumer GPU (1,792 GB/s, 78% more than the RTX 4090), which is what sets token speed. Buy this if you regularly run 30B+ parameter models.

Neither GPU is good for AI training — training requires 48GB+ VRAM (A6000, A100). Both are excellent for inference and fine-tuning LoRAs.

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Specs Comparison

The core difference is memory: 32GB vs 16GB determines which models you can run at full GPU speed. Memory bandwidth (1,792 vs 960 GB/s) directly impacts token generation speed since LLM inference is memory-bandwidth-bound.

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LLM Inference Benchmarks

Token generation speed is the metric that matters most for interactive use with Ollama and llama.cpp, and it is limited by memory bandwidth. Each cell below is either a published measurement with its source named or a bandwidth ceiling (NVIDIA's bandwidth ÷ the file size Ollama downloads), labelled as such. We have not run these cards ourselves.

Token Generation Speed (tok/s)

*Partially offloaded to system RAM — speed drops sharply, because the offloaded layers are read at system-RAM bandwidth (under 100 GB/s on dual-channel DDR5) instead of the card's.

The measured cells come from DatabaseMart's published single-RTX 5090 benchmark, run on Ollama 0.6.5 with the default 4-bit tags (read November 2, 2026). Two things stand out. Dense 32B models reach about half of the 5090's ceiling, so ~45 tok/s is what you will actually see in the Ollama workflow; optimized serving stacks (vLLM, TensorRT-LLM) push dense 32Bs higher. And small models reach a much smaller share (an 8B at 150 of a possible 364), because per-token overhead, not bandwidth, limits them on a card this fast. Note the 70B row: Llama 3.3 70B at Q4_K_M is a 43GB file, so it doesn't fit even in the 5090's 32GB — the Q2_K build (26.4GB) is the one that runs fully in VRAM, at a real quality cost.

Prompt Processing (Prefill) Speed

Prompt processing (reading your prompt before the first token) is limited by compute, not bandwidth, so it follows the shader and tensor-core counts: the 5090 has twice the CUDA and tensor cores of the 5080 (21,760 vs 10,752; 680 vs 336), so expect roughly twice the prefill rate. We do not print prefill numbers we cannot source. The RTX 5090's advantage grows with model size: on 8B models its token-generation ceiling is ~1.9x the 5080's, and on 32B models, which do not fit in 16GB at all, the 5080 has to offload to system RAM while the 5090 keeps everything in VRAM.

Key insight: LLM inference is memory-bandwidth-bound, not compute-bound. The 5090's 1,792 GB/s bandwidth is the primary reason it generates tokens faster, not its 2x CUDA cores. This is why the 5080's 960 GB/s still produces excellent results for models that fit in its 16GB.

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Which Models Fit in VRAM?

This is the most important table for choosing between these GPUs. If a model doesn't fit in VRAM, it spills to system RAM and slows down sharply, because system RAM has a small fraction of the card's bandwidth. Use our VRAM Calculator to check any model + quantization combo instantly.

RTX 5080 (16GB) — What Fits

RTX 5090 (32GB) — What Fits

The 32GB sweet spot: Models between 20-35B parameters (Qwen 2.5 32B, DeepSeek R1 32B, Yi-34B) are the "goldilocks zone" for the RTX 5090 — too large for the 5080, but perfect for the 5090 at full GPU speed.

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Stable Diffusion & Image AI

For image generation, both GPUs are excellent; the difference is what fits. We have not published it/s figures for either card, so this table is about memory. The 5080's 16GB handles most workflows:

The RTX 5090 shines for FLUX models (which use more VRAM) and batch generation workflows. For single-image SD/SDXL generation, the 5080 is more than sufficient.

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Value Analysis

Cost Per GB and Per Token of Speed

Ceilings are bandwidth ÷ file size, not measurements. There is no M4 Ultra; the Mac row is the M5 Ultra Mac Studio Apple announced in August 2026. Electricity at the US average of 18.16 cents/kWh (EIA, August 2026) adds at most about $0.10 an hour for a 5090 running at its 575W rating, $0.07 for a 5080.

When Each GPU Makes Financial Sense

RTX 5080 wins if you:

• Run models ≤14B parameters (the bulk of everyday local AI use)
• Have a GPU budget around $1,000-1,600 (the 5080 at launch price or street price)
• Prioritize power efficiency (360W vs 575W)
• Use Stable Diffusion for single-image workflows
• Want the best tokens-per-dollar at the 8B model tier

AMD covers the same 16GB tier for less money: the RX 9070 XT for local AI posts respectable tok/s once ROCm is configured, which is the real cost of going red instead of green.

RTX 5090 wins if you:

• Regularly run 30B-70B models (Qwen 32B, DeepSeek R1, Yi-34B)
• Want a dual-GPU path to 64GB total VRAM (two 5090s split models over PCIe)
• Plan to use it professionally (development, content creation)
• Want one GPU that handles everything from 7B to quantized 70B
• Use batch image generation or video AI workflows

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Upgrade Decision Guide

Upgrading from RTX 4090 (24GB)

The RTX 5090 gives you +8GB VRAM (32 vs 24) and 78% more memory bandwidth (33% faster on DatabaseMart's deepseek-r1:32b Ollama run). Worth upgrading if you're bottlenecked by 24GB VRAM on 32B models or need faster batch processing. Not worth it if your 4090 handles your current models comfortably.

The RTX 5080 is a downgrade in VRAM (16GB vs 24GB). Never "upgrade" from a 4090 to a 5080 for AI workloads.

Upgrading from RTX 3090 / 4080

Both are significant upgrades. The RTX 5080 gives you GDDR7 bandwidth and Blackwell tensor cores — excellent value at its $999 launch price, less so at November's $1,579 street price. The 5090 doubles your usable model range if coming from 16GB cards.

Upgrading from RTX 3060/3070/4060 (8-12GB)

Either GPU is transformative. The RTX 5080 moves you from "can barely run 7B" to "runs every 7B-14B model fully on the GPU", with a bandwidth ceiling of ~195 tok/s on an 8B. This is the upgrade that matters most for local AI beginners. If the 5080 is above your budget, the next step down is the 12GB card — see RTX 5070 for local AI: what 12GB runs, and when to pay for 16GB before you trade away the VRAM.

The Multi-GPU Alternative

Two RTX 5090s ($3,998 at launch prices, about $6,680 at the November 2 street price) provide 64GB VRAM, split over PCIe 5.0 — enough for a full-quality 70B Q4_K_M with room for a long context. DatabaseMart's published dual-5090 Ollama run recorded 26.85 tok/s on llama3.3:70b, ahead of the 24.34 its single H100 managed on the same model, at a fraction of an H100's price. For serious local AI work, dual 5090s are the most cost-effective path to datacenter-class 70B inference.

Dual RTX 5080 vs One RTX 5090

Two 5080s give the same 32GB total as one 5090, so the question comes up often. What fits is the same; the speed is not. Ollama and llama.cpp split a model by layers across the two cards, and the cards take turns reading their half, so a pair of 5080s has the bandwidth ceiling of one 5080 (960 GB/s), not two. On a 32B Q4 model (19.9GB) that is ~48 tok/s ceiling for the pair against ~90 for a single 5090.

The single 5090 is faster, draws less power and needs no multi-GPU board. A pair of 5080s only makes sense if you already own one 5080, or if you want to run two separate 16GB models side by side.

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Sources

• NVIDIA GeForce graphics card comparison — VRAM, memory bandwidth and power ratings for the RTX 5090, 5080, 5070 Ti and 3090 (read November 2, 2026)
• NVIDIA marketplace — Founders Edition prices and stock (RTX 5090 $1,999 and RTX 5080 $999, both out of stock; RTX 5070 Ti from $1,269.99; read November 2, 2026)
• Walmart RTX 5090 and RTX 5080 listings — GIGABYTE AORUS RTX 5090 $3,340; PNY RTX 5080 OC $1,579 (read November 2, 2026)
• Ollama model library — file sizes used for every ceiling (llama3.1:8b 4.9GB, qwen2.5:14b 9.0GB, qwen2.5:32b and deepseek-r1:32b 20GB, llama3.3:70b 43GB, its Q2_K build 26GB)
• EIA Electric Power Monthly, table 5.6.A — US average residential price 18.16 cents/kWh, August 2026
• RTX 5090 LLM Benchmarks — RunPod — Comprehensive LLM inference benchmarks
• RTX 5080 AI Benchmarks — Micro Center — Real-world LLM performance testing
• RTX 5090 & 5080 AI Review — Puget Systems — Professional AI workload analysis
• Single RTX 5090 Ollama Benchmarks — DatabaseMart — Ollama 0.6.5 token generation: llama3.1:8b 149.95, qwen2.5:14b 89.93, qwen2.5:32b 45.07, deepseek-r1:32b 45.51 tok/s; RTX 4090 34.22 on deepseek-r1:32b
• Dual RTX 5090 Ollama Benchmarks — DatabaseMart — Ollama 0.6.5: llama3.3:70b 26.85 and deepseek-r1:70b 27.03 tok/s on two 5090s; single H100 24.34 on llama3.3:70b
• RTX 5090 10K Tokens/sec Results — Hardware Corner — Prompt processing and context length benchmarks
• RTX 5090 vs 5080 — BOXX — Specification comparison and real-world tests

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FAQ