What AI models can NVIDIA GeForce RTX 5080 run?

The NVIDIA GeForce RTX 5080 can run 27 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, Arcee Trinity Nano 6B. See the full compatibility table above for speeds and quality ratings.

Is NVIDIA GeForce RTX 5080 good for AI coding?

Yes. With 16 GB, the NVIDIA GeForce RTX 5080 handles single-model coding workflows well at the Capable tier.

How much VRAM does NVIDIA GeForce RTX 5080 have?

The NVIDIA GeForce RTX 5080 has 16 GB of GDDR7 VRAM with 960 GB/s bandwidth.

Can NVIDIA GeForce RTX 5080 run 70B models?

70B models can run on the NVIDIA GeForce RTX 5080 with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.

Is NVIDIA GeForce RTX 5080 worth it for AI?

At $1,099, the NVIDIA GeForce RTX 5080 offers 16 GB VRAM and runs 27 AI models. It works for smaller models and experimentation.

Desktop GPU

NVIDIA GeForce RTX 5080

16 GB GDDR7 · 960 GB/s

From

$1,099

Estimated street price

VRAM

16 GB

Bandwidth

960 GB/s

TDP

360W

Models

Tier

Capable

The NVIDIA GeForce RTX 5080 with 16 GB GDDR7 VRAM can handle 27 AI models across reasoning, chat, coding. Best performance: Gemma 4 26B-A4B at 328 tok/s (excellent). For AI coding workflows, it supports the Capable AI Coding tier, handling single model workflows well. Current price: approximately $1,099.

Source: OwnRig methodology

VRAM

16 GB

Bandwidth

960 GB/s

Memory Type

GDDR7

TDP

360W

Form Factor

3-slot, 304mm

Builder Capability: Capable AI Coding

Runs 16-22B coding models comfortably, or 32B at reduced quality. Handles single model workflows well.

Software

Inference Backends

The software stacks that matter most for real-world inference on this device.

CUDA

production

Primary high-performance backend for NVIDIA inference workloads.

Vulkan

stable

Fallback backend for llama.cpp and related local runtimes.

What it can run

27 models


Arcee Trinity Mini 26B	Q3_K_M	91 tok/s	Excellent
Arcee Trinity Nano 6B	Q8_0	169 tok/s	Excellent
DeepSeek R1 Distill Qwen 7B	Q8_0	85 tok/s	Excellent
DeepSeek V3	Q2_K	–	Not viable
Gemma 3 12B	Q5_K_M	72 tok/s	Excellent
Gemma 3 27B	Q3_K_M	18 tok/s	Acceptable
Gemma 4 26B-A4B	Q3_K_M	328 tok/s	Excellent
Gemma 4 31B	Q3_K_M	22 tok/s	Good
Gemma 4 E2B	Q8_0	144 tok/s	Excellent
Gemma 4 E4B	Q8_0	89 tok/s	Excellent
GigaChat Lightning 10B	Q8_0	99 tok/s	Acceptable
Llama 3.1 8B Instruct	Q8_0	92 tok/s	Excellent
Llama 3.2 11B Vision	Q8_0	105 tok/s	Excellent
Llama 3.2 1B Instruct	Q8_0	230 tok/s	Excellent
Llama 3.2 3B Instruct	Q8_0	160 tok/s	Excellent
NVIDIA Nemotron-3-super-120B-A12B	Q2_K	–	Not viable
Phi-4 14B	Q4_K_M	52 tok/s	Excellent
Phi-4 Mini	Q8_0	150 tok/s	Excellent
Qwen 2.5 Coder 7B Instruct	Q8_0	88 tok/s	Excellent
Qwen3-14B Instruct	Q8_0	29 tok/s	Good
Qwen3.5-122B-A10B	Q3_K_M	–	Not viable
Qwen3.5-27B	Q3_K_M	45 tok/s	Acceptable
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-27B	Q3_K_M	45 tok/s	Acceptable
Stable Diffusion 3.5 Large	FP16	–	Excellent
Stable Diffusion XL 1.0	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Excellent

Showing 27 of 27 entries

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FAQ

Frequently Asked Questions

What AI models can NVIDIA GeForce RTX 5080 run?: The NVIDIA GeForce RTX 5080 can run 27 AI models. Top performers include Gemma 4 26B-A4B, Llama 3.2 1B Instruct, Arcee Trinity Nano 6B. See the full compatibility table above for speeds and quality ratings.
Is NVIDIA GeForce RTX 5080 good for AI coding?: Yes. With 16 GB, the NVIDIA GeForce RTX 5080 handles single-model coding workflows well at the Capable tier.
How much VRAM does NVIDIA GeForce RTX 5080 have?: The NVIDIA GeForce RTX 5080 has 16 GB of GDDR7 VRAM with 960 GB/s bandwidth.
Can NVIDIA GeForce RTX 5080 run 70B models?: 70B models can run on the NVIDIA GeForce RTX 5080 with CPU offloading, but performance will be reduced. Consider a GPU with 48GB+ VRAM for full-speed 70B inference.
Is NVIDIA GeForce RTX 5080 worth it for AI?: At $1,099, the NVIDIA GeForce RTX 5080 offers 16 GB VRAM and runs 27 AI models. It works for smaller models and experimentation.

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