Question 1

What AI models can run on A2?

Accepted Answer

The A2 (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 A2?

Accepted Answer

We don't currently have cloud pricing data for the A2. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is A2 good for fine-tuning?

Accepted Answer

The A2 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 A2 have?

Accepted Answer

The A2 has 16 GB of VRAM (GDDR6). It also offers 200 GB/s memory bandwidth, 4.5 TFLOPS of FP16 compute.

Question 5

What quantization formats work on A2?

Accepted Answer

The A2 supports the following quantization formats: FP16 (full precision), Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~3B parameters. Q4 quantization can fit models requiring up to ~64 GB at FP16 into 16 GB.

A2

Specifications

Cloud pricing

Compatible models

Compare this GPU

Frequently asked questions

VRAM	16 GB
Memory type	GDDR6
Bandwidth	200 GB/s
FP16 TFLOPS	4.5
FP8 support	No

A2