Best Gemma 4 GPU Hosting Providers (2026)

Gemma 4 is Google DeepMind’s family of open AI models, built from the same research as Gemini. Released in 2026 under the Apache 2.0 license, it is free for commercial use. Every model is multimodal: it understands images and video, and the smaller models also understand audio.

🧮 Not sure which GPU you need? Try our AI Model GPU Calculator: pick Gemma 4, your context length and usage, and see the VRAM needed, the cheapest hosting and the API cost side by side.

The best part for self-hosting is size. The flagship Gemma 4 31B fits on a single 24 GB GPU in 4-bit, and the 26B A4B Mixture-of-Experts model runs fast because only about 4B parameters are active per token. You do not need an expensive multi-GPU cluster to run a strong AI model.

We compared the best GPU hosting providers for Gemma 4, from $0.30/hour cloud GPUs to flat-rate monthly servers. Below you will find VRAM requirements for every Gemma 4 model, provider prices and a deployment guide.

Quick Comparison: Best Gemma 4 GPU Hosting (September 2026)

Provider	Best GPU for Gemma 4	Starting Price	Billing	Best For
GPU Mart	RTX 4090 24GB / RTX A6000 48GB	$409/month	Monthly	24/7 Gemma AI apps on a budget
HOSTKEY	RTX 4090 / RTX 5090 32GB	€279/month	Hourly & monthly	EU-hosted Gemma servers
RunPod	RTX 4090 / RTX 5090 / L40S	$0.34/hour	Per second	Serverless Gemma endpoints
Vast.ai	RTX 3090 / RTX 4090	~$0.30/hour	Per second	Cheapest testing and fine-tuning
Google Cloud	L4 24GB / H100	~$0.70/hour (L4)	Hourly	Managed Gemma on Vertex AI
Vultr	Fractional GPUs / L40S	Few cents/hour	Hourly	Small Gemma models worldwide
TensorDock	RTX 4090 / A100 VMs	$0.12/hour	Hourly	Full VMs at low prices
OVHcloud	L4 / L40S	$0.60/hour	Hourly	EU sovereignty, free traffic

Which Gemma 4 Model Can You Run? (VRAM Requirements)

The figures below are approximate VRAM for the model weights only. Add 10–30% for the KV cache, and more if you use the full 256K context window. Google publishes official quantization-aware-trained (QAT) 4-bit versions, so 4-bit Gemma 4 loses very little quality.

Gemma 4 Model	Parameters	VRAM (4-bit)	VRAM (BF16)	Recommended GPU
Gemma 4 E2B	2.3B effective (5.1B total)	~3 GB	~10 GB	Any 8 GB GPU, T4, RTX 3060
Gemma 4 E4B	4.5B effective (8B total)	~5 GB	~16 GB	RTX 4060 Ti, RTX A4000, L4
Gemma 4 12B	12B dense	~7 GB	~24 GB	RTX 4070, RTX 4090, L4
Gemma 4 26B A4B (MoE)	25.2B total, 3.8B active	~15 GB	~51 GB	RTX 4090, RTX 5090, L40S
Gemma 4 31B	30.7B dense	~18 GB	~62 GB	RTX 4090 / 5090 (4-bit), A100 80GB or H100 (BF16)

Rule of thumb: For most AI apps, Gemma 4 31B in 4-bit on one RTX 4090 or RTX 5090 is the sweet spot for quality. If you need speed with many users, the 26B A4B MoE model gives near-31B quality at much higher tokens per second, because only 3.8B parameters are active per token.

What Are the Best GPU Hosting Providers for Gemma 4?

1. GPU Mart

GPU Mart is our top pick if you want to run Gemma 4 around the clock. Their control panel offers pre-configured AI apps, including Ollama and Gemma, so you can have a working Gemma endpoint minutes after your server is delivered.

An RTX 4090 server at $409/month runs Gemma 4 31B or 26B A4B in 4-bit with room for a long context. The RTX A6000 (48 GB) plan is a good upgrade if you want 8-bit quality or want to serve Gemma to many users at once.

2. HOSTKEY

HOSTKEY is a great choice for a private, EU-hosted Gemma 4 assistant. Servers can come with Ollama and Open WebUI pre-installed, so you get a ChatGPT-style AI interface backed by Gemma 4 without any setup.

The RTX 5090 (32 GB) is a very good match for Gemma 4 31B. It fits the model in 8-bit with room for context, and Blackwell’s FP4/FP8 support speeds up inference. For the full BF16 model, choose an RTX PRO 6000 with 96 GB.

3. RunPod

RunPod is the easiest way to try Gemma 4 by the hour. Start the vLLM template, set the model to google/gemma-4-31B-it-qat-w4a16-ct, and you will have an OpenAI-compatible AI endpoint on a $0.34/hour RTX 4090.

Because Gemma 4 models are small, they load quickly. That makes them a good fit for RunPod Serverless, where your endpoint scales to zero when nobody is using it and only costs money when requests come in.

4. Vast.ai

Vast.ai is a GPU marketplace and often the cheapest place to run Gemma 4. RTX 4090s regularly cost around $0.30/hour, and even an older RTX 3090 (24 GB) handles Gemma 4 31B in 4-bit.

It is also a great place to fine-tune Gemma 4. A LoRA fine-tune of the 12B model fits on a single 24 GB card with tools like Unsloth. For the 31B model, rent an A100 80GB from about $0.43/hour.

5. Google Cloud

Gemma is a Google model, so it is no surprise that Google Cloud has the smoothest managed experience. Vertex AI Model Garden lets you deploy any Gemma 4 model to an autoscaling endpoint in a few clicks, with no servers to manage.

If you want to run it yourself, a single L4 (24 GB) G2 VM runs Gemma 4 31B in 4-bit and is one of the cheapest data-center GPUs on GCP. New accounts get $300 in free credit, which is enough to test Gemma for a long time.

6. Vultr

Vultr’s fractional GPUs are a great way to host the small Gemma 4 E2B, E4B and 12B models. You pay only for a slice of a GPU, which is enough for a lightweight AI assistant, a classification service or image captioning.

For the 31B model, an L40S at $1.67/hour runs it comfortably in 8-bit. With 32 regions, Vultr is a good choice if you want to serve Gemma close to users around the world.

7. TensorDock

TensorDock gives you full virtual machines instead of containers, which helps if you want to run Gemma 4 next to your own database, API or vector store for retrieval-augmented generation (RAG). Consumer GPUs start from $0.12/hour, and an RTX 4090 VM is plenty for Gemma 4 31B in 4-bit.

8. OVHcloud

OVHcloud is Europe’s largest cloud provider and a safe choice if your Gemma deployment has to stay under EU jurisdiction. An L40S (48 GB) instance runs Gemma 4 31B in 8-bit or the 26B A4B model in BF16, with plenty of room for long documents.

OVHcloud does not charge for traffic, so you can serve image-heavy multimodal AI requests to Gemma without surprise bandwidth bills.

How to Deploy Gemma 4 on a GPU Server

Once your server is running (Ubuntu + NVIDIA drivers), you can serve Gemma 4 in minutes.

Option 1: Ollama (easiest)

curl -fsSL https://ollama.com/install.sh | sh
ollama run gemma4:31b

Other tags: gemma4:e2b, gemma4:e4b, gemma4:12b and gemma4:26b. Add Open WebUI on top to get a private ChatGPT-style AI chat interface.

Option 2: vLLM (production, OpenAI-compatible API)

pip install vllm
vllm serve google/gemma-4-31B-it-qat-w4a16-ct \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.92

This uses Google’s official 4-bit QAT checkpoint and fits on a 24 GB GPU. On an A100 80GB or H100, serve google/gemma-4-31B-it in full BF16 instead. vLLM exposes an OpenAI-compatible endpoint on port 8000, so Open WebUI, LangChain or your own app can use Gemma 4 as a drop-in replacement for the OpenAI API.

How to Choose the Right Gemma 4 Hosting

• Pick the model first. E2B/E4B for edge and very cheap AI tasks, 12B for a good balance, 26B A4B for fast multi-user chat, 31B for the best quality.
• One GPU is enough. Every Gemma 4 model runs on a single 24–32 GB GPU in 4-bit. Do not pay for multi-GPU clusters.
• Hourly vs monthly. For experiments and fine-tuning, use RunPod, Vast.ai or TensorDock. For a 24/7 AI chatbot or API, a fixed-price monthly server from GPU Mart or HOSTKEY gives you a whole machine with predictable costs and is often cheaper than reliable (secure-cloud) hourly GPUs.
• Managed or self-hosted. If you do not want to manage servers, Vertex AI on Google Cloud is the simplest way to run Gemma.
• Data location. For EU customer data, pick OVHcloud or HOSTKEY’s EU data centers.

FAQ

What is the cheapest way to host Gemma 4?

For short jobs, an RTX 4090 on Vast.ai or RunPod Community Cloud costs about $0.30–$0.35/hour and runs any Gemma 4 model in 4-bit. For 24/7 use, a monthly RTX 4090 server from GPU Mart (about $409/month) is usually cheaper. For the tiny E2B and E4B models, Vultr’s fractional GPUs cost only a few cents per hour.

Can Gemma 4 run on a single GPU?

Yes. All Gemma 4 models fit on one GPU. The 31B flagship needs about 18 GB of VRAM in 4-bit, so a single RTX 4090, RTX 5090, L4 or A6000 is enough. In full BF16 precision it needs an 80 GB card such as an A100 or H100.

Is Gemma 4 free for commercial use?

Yes. Gemma 4 is released under the Apache 2.0 license, which allows commercial use, modification and redistribution. This is a change from earlier Gemma versions, which used Google’s custom Gemma license.

Gemma 4 vs Qwen: which open AI model should I host?

Both run well on a single 24–32 GB GPU. Gemma 4 is stronger at multimodal tasks (images, video and audio) and has official 4-bit QAT versions. Qwen is often stronger at coding and has larger MoE models for multi-GPU servers. Many teams try both on the same GPU server and keep the one that performs best on their own data.

Conclusion

Gemma 4 is one of the easiest strong open AI models to self-host. For most people, the best Gemma 4 GPU hosting is a single RTX 4090 or RTX 5090 running Gemma 4 31B or 26B A4B. Choose GPU Mart or HOSTKEY for a fixed monthly price, RunPod or Vast.ai to pay by the hour, or Google Cloud’s Vertex AI if you want Gemma fully managed.

Prices were checked in September 2026 and can change often. Confirm current pricing on each provider’s website before you order.