Question 1

What AI models can run on RTX 5080?

Accepted Answer

The RTX 5080 (16 GB VRAM) can run models including gemma-3-4b-pt, gemma-4-E2B-it, gemma-3-4b-it, Qwen2.5-1.5B-Instruct, Qwen2.5-3B-Instruct and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 16 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX 5080?

Accepted Answer

We don't currently have cloud pricing data for the RTX 5080. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is RTX 5080 good for fine-tuning?

Accepted Answer

The RTX 5080 can handle fine-tuning with some constraints. With 16 GB of VRAM, it's suitable for LoRA/QLoRA fine-tuning of models up to ~7B parameters. Full fine-tuning is limited to smaller models (~1-3B parameters). Techniques like gradient checkpointing and mixed precision help maximize what fits.

Question 4

How much VRAM does RTX 5080 have?

Accepted Answer

The RTX 5080 has 16 GB of VRAM (GDDR7). It also offers 960 GB/s memory bandwidth, 112.6 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX 5080?

Accepted Answer

The RTX 5080 supports the following quantization formats: FP16 (full precision), FP8, Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~3B parameters. FP8 halves memory vs FP16 with minimal quality loss. Q4 quantization can fit models requiring up to ~64 GB at FP16 into 16 GB.

RTX 5080

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	16 GB
Memory type	GDDR7
Bandwidth	960 GB/s
FP16 TFLOPS	112.6
FP8 support	Yes