Question 1

What AI models can run on A16?

Accepted Answer

The A16 (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 A16?

Accepted Answer

We don't currently have cloud pricing data for the A16. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is A16 good for fine-tuning?

Accepted Answer

The A16 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 A16 have?

Accepted Answer

The A16 has 16 GB of VRAM (GDDR6). It also offers 400 GB/s memory bandwidth, 17.5 TFLOPS of FP16 compute.

Question 5

What quantization formats work on A16?

Accepted Answer

The A16 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.

A16

Specifications

Cloud pricing

Compatible models

Compare this GPU

Frequently asked questions

VRAM	16 GB
Memory type	GDDR6
Bandwidth	400 GB/s
FP16 TFLOPS	17.5
FP8 support	No

A16