Arrakis / Models / InternVL3_5-GPT-OSS-20B-A4B-Preview-HF

InternVL3_5-GPT-OSS-20B-A4B-Preview-HF GPU requirements

InternVL3_5-GPT-OSS-20B-A4B-Preview-HF has 21B parameters. To serve it, plan for about 51 GB of GPU memory in 16-bit, 25 GB in 8-bit or 13 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 32 experts, 4 of them active for each token. All of them stay in GPU memory, so plan memory for all 21B parameters.

What it takes to serve it

GPU memory to run it for inference, with the smallest GPU setup that holds it and the lowest price on Arrakis right now.

PrecisionMemory and the smallest setup
16-bit51 GB: one H100 or A100, H100 from $2.25 per GPU-hour
8-bit25 GB: one RTX 5090, from $0.58 per GPU-hour
4-bit13 GB: one RTX 4090 or RTX 3090, RTX 3090 from $0.19 per GPU-hour

What it takes to fine-tune it

QLoRA trains adapters on a 4-bit frozen model, LoRA on a 16-bit one, and full fine-tuning updates every weight. Figures are before activations, which grow with batch size and sequence length.

MethodMemory and the smallest setup
QLoRA15 GB: one RTX 4090 or RTX 3090, RTX 3090 from $0.19 per GPU-hour
LoRA51 GB: one H100 or A100, H100 from $2.25 per GPU-hour
Full fine-tuning340 GB: 2 × B200, from $13.58 per hour for all 2

About the model

From its card and files on Hugging Face. Memory figures here follow the GPU memory guide.

Parameters
21B
Active for each token
4B
Experts
32
Experts for each token
4
Context window (tokens)
131,072
Published in
BF16
License
apache-2.0
Released
2025-08-29
Weights
OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF

Deploy it on Arrakis

Open Models in the console, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine. Or run it yourself on any machine with enough GPU memory:

vLLM
vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --tensor-parallel-size 1

Questions

Live prices for every GPU are on the GPU prices page.

How much GPU memory does InternVL3_5-GPT-OSS-20B-A4B-Preview-HF need?
To serve it, about 51 GB in 16-bit, 25 GB in 8-bit, 13 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 15 GB with QLoRA, 51 GB with LoRA and 340 GB for full fine-tuning with Adam, before activations.
Can I run InternVL3_5-GPT-OSS-20B-A4B-Preview-HF on one GPU?
Yes. In 16-bit it needs about 51 GB, which fits on one H100 or A100.
What does it cost to run InternVL3_5-GPT-OSS-20B-A4B-Preview-HF?
In 16-bit it needs one H100 or A100; in 4-bit, one RTX 4090 or RTX 3090. Each setup on this page shows the lowest price per GPU-hour on Arrakis right now, and you pay by the hour from a prepaid balance.
How do I deploy InternVL3_5-GPT-OSS-20B-A4B-Preview-HF on Arrakis?
Open Models in the Arrakis console, paste OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine.