Question 1

What AI models can run on V100 32GB?

Accepted Answer

The V100 32GB (32 GB VRAM) can run models including Llama-3.2-3B-Instruct-QLORA_INT4_EO8, intent-aware-lfqa-qwen3-8b-multiview, Llama-2-7b, MediPhi-MedWiki, intent-aware-lfqa-qwen3-8b-intent-explicit and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 32 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for V100 32GB?

Accepted Answer

We don't currently have cloud pricing data for the V100 32GB. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is V100 32GB good for fine-tuning?

Accepted Answer

The V100 32GB can handle fine-tuning with some constraints. With 32 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 V100 32GB have?

Accepted Answer

The V100 32GB has 32 GB of VRAM (HBM2). It also offers 900 GB/s memory bandwidth, 28.3 TFLOPS of FP16 compute.

Question 5

What quantization formats work on V100 32GB?

Accepted Answer

The V100 32GB 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 ~108B parameters (by VRAM estimate) fit on the V100 32GB.

V100 32GB

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	32 GB
Memory type	HBM2
Bandwidth	900 GB/s
FP16 TFLOPS	28.3
FP8 support	No