Question 1

What AI models can run on RTX 4090?

Accepted Answer

The RTX 4090 (24 GB VRAM) can run models including Llama-2-7b-chat, Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8, FastContext-1.0-4B-RL, Llama-3.2-1B-Instruct-QLORA_INT4_EO8, HARC-Llama-3.1-8B-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 24 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX 4090?

Accepted Answer

The cheapest on-demand option for the RTX 4090 is Vast.ai at $0.40/hr (1x-RTX_4090-32-65536). Spot/preemptible pricing is available at $0.00/hr. We track pricing across 16 providers to help you find the best deal.

Question 3

Is RTX 4090 good for fine-tuning?

Accepted Answer

The RTX 4090 can handle fine-tuning with some constraints. With 24 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 4090 have?

Accepted Answer

The RTX 4090 has 24 GB of VRAM (GDDR6X). It also offers 1008 GB/s memory bandwidth, 165.2 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX 4090?

Accepted Answer

The RTX 4090 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 ~81B parameters (by VRAM estimate) fit on the RTX 4090.

Provider	On-demand	Spot	Actions
Vast.ai	$0.400/hr	$0.000/hr	Rent
RunPod	$0.690/hr	$0.690/hr	Rent Get $5–$500 free

RTX 4090

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	24 GB
Memory type	GDDR6X
Bandwidth	1008 GB/s
FP16 TFLOPS	165.2
FP8 support	Yes