Question 1

What AI models can run on A30?

Accepted Answer

The A30 (24 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 24 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for A30?

Accepted Answer

We don't currently have cloud pricing data for the A30. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is A30 good for fine-tuning?

Accepted Answer

The A30 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 A30 have?

Accepted Answer

The A30 has 24 GB of VRAM (HBM2). It also offers 933 GB/s memory bandwidth, 165 TFLOPS of FP16 compute.

Question 5

What quantization formats work on A30?

Accepted Answer

The A30 supports the following quantization formats: FP16 (full precision), Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~7B parameters. Q4 quantization lets models up to ~81B parameters (by VRAM estimate) fit on the A30.

A30

Specifications

Cloud pricing

Compatible models

Compare this GPU

Frequently asked questions

VRAM	24 GB
Memory type	HBM2
Bandwidth	933 GB/s
FP16 TFLOPS	165
FP8 support	No

A30