Question 1

What AI models can run on RTX 4000 Ada?

Accepted Answer

The RTX 4000 Ada (20 GB VRAM) can run models including gemma-3-4b-pt, gemma-4-E4B-it, gemma-4-E2B-it, gemma-3-4b-it, Qwen2.5-7B-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 20 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX 4000 Ada?

Accepted Answer

We don't currently have cloud pricing data for the RTX 4000 Ada. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is RTX 4000 Ada good for fine-tuning?

Accepted Answer

The RTX 4000 Ada can handle fine-tuning with some constraints. With 20 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 4000 Ada have?

Accepted Answer

The RTX 4000 Ada has 20 GB of VRAM (GDDR6). It also offers 360 GB/s memory bandwidth, 52.6 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX 4000 Ada?

Accepted Answer

The RTX 4000 Ada 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 ~80 GB at FP16 into 20 GB.

RTX 4000 Ada

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	20 GB
Memory type	GDDR6
Bandwidth	360 GB/s
FP16 TFLOPS	52.6
FP8 support	Yes