Question 1

What AI models can run on RTX 5090?

Accepted Answer

The RTX 5090 (32 GB VRAM) can run models including Llama-2-7b-chat, Llama-3.2-1B-Instruct-QLORA_INT4_EO8, MediPhi-Instruct, Llama-3.2-3B-Instruct-SpinQuant_INT4_EO8, Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8 and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 32 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX 5090?

Accepted Answer

The cheapest on-demand option for the RTX 5090 is Vast.ai at $0.44/hr (1x-RTX_5090-32-65536). Spot/preemptible pricing is available at $0.00/hr. We track pricing across 11 providers to help you find the best deal.

Question 3

Is RTX 5090 good for fine-tuning?

Accepted Answer

The RTX 5090 can handle fine-tuning with some constraints. With 32 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 5090 have?

Accepted Answer

The RTX 5090 has 32 GB of VRAM (GDDR7). It also offers 1792 GB/s memory bandwidth, 209.5 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX 5090?

Accepted Answer

The RTX 5090 supports the following quantization formats: FP16 (full precision), FP8, Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~7B parameters. FP8 halves memory vs FP16 with minimal quality loss. Q4 quantization lets models up to ~108B parameters (by VRAM estimate) fit on the RTX 5090.

Provider	On-demand	Spot	Actions
Vast.ai	$0.440/hr	$0.000/hr	Rent
RunPod	$0.990/hr	$0.990/hr	Rent Get $5–$500 free

RTX 5090

Specifications

Cloud pricing

Compatible models

Compare this GPU

Frequently asked questions

VRAM	32 GB
Memory type	GDDR7
Bandwidth	1792 GB/s
FP16 TFLOPS	209.5
FP8 support	Yes