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Gemma Hosting Service: Auto-Deploy Gemma 3/2 LLMs Efficiently

Name: Gemma Hosting: Deploy Gemma3 4B/12B/27B with Ollama, vLLM, TGI & GGML
Brand: Cloud Clusters
Price: 269 USD
Availability: InStock
Rating: 4.8 (5968 reviews)

Unlock the full potential of Google DeepMind’s Gemma 2B, 7B, 9B, and 27B models with our optimized Gemma Hosting solutions. Whether you prefer low-latency inference via vLLM, user-friendly setup with Ollama, enterprise-grade performance through TensorRT-LLM, or offline deployment using GGML, our infrastructure supports it all. Ideal for AI research, chatbot APIs, fine-tuning, or private in-house applications, Gemma Hosting ensures scalable performance with GPU-powered servers. Deploy Gemma Service securely and efficiently—tailored for developers, enterprises, and innovators.

Pre-installed Gemma3-27B LLM Hosting

Cloud Clusters offers best budget GPU servers for Gemma3 LLMs. You'll get pre-installed Open WebUI + Ollama + Gemma3-27B, it is a popluar way to self-hosted LLM models.

Advanced GPU Dedicated Server - A5000

$ 269.00/mo

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128GB RAM
GPU: Nvidia Quadro RTX A5000
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 8192
Tensor Cores: 256
GPU Memory: 24GB GDDR6
FP32 Performance: 27.8 TFLOPS

Enterprise GPU Dedicated Server - RTX 4090

$ 409.00/mo

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256GB RAM
GPU: GeForce RTX 4090
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ada Lovelace
CUDA Cores: 16,384
Tensor Cores: 512
GPU Memory: 24 GB GDDR6X
FP32 Performance: 82.6 TFLOPS

New Arrival

Advanced GPU VPS - RTX 5090

$ 339.00/mo

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96GB RAM
32 CPU Cores
400GB SSD
500Mbps Unmetered Bandwidth

Once per 2 Weeks Backup
OS: Linux / Windows 10/ Windows 11
Dedicated GPU: GeForce RTX 5090
CUDA Cores: 21,760
Tensor Cores: 680
GPU Memory: 32GB GDDR7
FP32 Performance: 109.7 TFLOPS

Enterprise GPU Dedicated Server - A100

$ 639.00/mo

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256GB RAM
GPU: Nvidia A100
Dual 18-Core E5-2697v4
240GB SSD + 2TB NVMe + 8TB SATA
100Mbps-1Gbps
OS: Windows / Linux

Single GPU Specifications:
Microarchitecture: Ampere
CUDA Cores: 6912
Tensor Cores: 432
GPU Memory: 40GB HBM2
FP32 Performance: 19.5 TFLOPS

Gemma Hosting with Ollama — GPU Recommendation

Deploy and run Google’s Gemma Service, such as Gemma3-27B and 12B, using Ollama — a powerful, user-friendly platform for managing large language models. With one-line model deployment, GPU acceleration, and support for custom prompts and workflows, Ollama makes Gemma hosting seamless for developers and teams. Ideal for local inference, private deployments, and lightweight LLM applications on servers with 8GB–24GB+ VRAM.

Model Name	Size (4-bit Quantization)	Recommended GPUs	Tokens/s
gemma3:1b	815MB	P1000 < GTX1650 < GTX1660 < RTX2060	28.90-43.12
gemma2:2b	1.6GB	P1000 < GTX1650 < GTX1660 < RTX2060	19.46-38.42
gemma3:4b	3.3GB	GTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX5060	28.36-80.96
gemma2:9b	5.4GB	T1000 < RTX3060 Ti < RTX4060 < RTX5060	12.83-21.35
gemma3n:e2b	5.6GB	T1000 < RTX3060 Ti < RTX4060 < RTX5060	30.26-56.36
gemma3n:e4b	7.5GB	A4000 < A5000 < V100 < RTX4090	38.46-70.90
gemma3:12b	8.1GB	A4000 < A5000 < V100 < RTX4090	30.01-67.92
gemma2:27b	16GB	A5000 < A6000 < RTX4090 < A100-40gb < H100 = RTX5090	28.79-47.33
gemma3:27b	17GB	A5000 < RTX4090 < A100-40gb < H100 = RTX5090	28.79-47.33

Gemma Hosting with vLLM + Hugging Face — GPU Recommendation

Host and deploy Google’s Gemma Service efficiently using the vLLM inference engine integrated with Hugging Face Transformers. This setup enables lightning-fast, memory-optimized inference for models like Gemma3-12B and 27B, thanks to vLLM’s advanced kernel fusion, continuous batching, and tensor parallelism. By leveraging Hugging Face’s ecosystem and vLLM’s scalability, developers can build robust APIs, chatbots, and research tools with minimal latency and resource usage. Ideal for GPU servers with 24GB+ VRAM.

Model Name	Size (16-bit Quantization)	Recommended GPU(s)	Concurrent Requests	Tokens/s
google/gemma-3n-E4B-it google/gemma-3-4b-it	8.1GB	A4000 < A5000 < V100 < RTX4090	50	2014.88-7214.10
google/gemma-2-9b-it	18GB	A5000 < A6000 < RTX4090	50	951.23-1663.13
google/gemma-3-12b-it google/gemma-3-12b-it-qat-q4_0-gguf	23GB	A100-40gb < 2*A100-40gb< H100	50	477.49-4193.44
google/gemma-2-27b-it google/gemma-3-27b-it google/gemma-3-27b-it-qat-q4_0-gguf	51GB	2*A100-40gb < A100-80gb < H100	50	1231.99-1990.61

✅ Explanation:

Recommended GPUs: From left to right, performance from low to high
Tokens/s: from benchmark data.

What is Gemma Hosting?

Gemma Hosting is the deployment and serving of Google’s Gemma language models (like Gemma 2B and Gemma 7B) on dedicated hardware or cloud infrastructure for various applications such as chatbots, APIs, or research environments.

Gemma is a family of open-source, lightweight large language models (LLMs) released by Google, designed for efficient inference on consumer GPUs and enterprise workloads. They are smaller and more efficient than models like GPT or LLaMA, making them ideal for cost-effective hosting.

How Pre-Installed Gemma Hosting Works?

1. High-End GPU Server

You choose a VPS or dedicated server with a high-performance NVIDIA GPU (e.g. A5000, A100, RTX 4090 / 5090). CUDA, cuDNN, yTorch/Transformers, and all Gemma dependencies are installed on Ubuntu 24.04.

2. Pre-Installed Gemma 3 Models

The Ollama hosting platform is pre-downloaded, and Gemma 3 4B, 12B, and 27B 4-bit quantized models are available. Checkpoints are already placed on fast NVMe storage for memory-efficient inference.

3. Open WebUI Integration

Your hosting dashboard lists a unique URL and port. You can open it in any browser to chat or prompt models—no command line required. From the WebUI, you can select 4B, 12B, or 27B, load custom prompts, and adjust build settings (temperature, maximum tokens, etc.). Optional login or team accounts for collaborative work.

4. Developer and Root Access

SSH/Root Login: You retain root access, allowing you to perform advanced tasks such as fine-tuning, integrating APIs, or installing additional frameworks. Call Gemma models from your own applications or pipelines using Python (Transformers, vLLM) or REST endpoints.

Detail Display: Open WebUI Integration

The 'additional software' section in the client control panel provides a unique URL and port for Open WebUI. No additional setup is required; simply copy and paste the URL into any modern browser to access the chat interface.

Detail Display: Start Chatting

You'll need to set a username and password to log in to protect your access. Gemma3-27B is selected by default, allowing you to start a conversation directly in the user interface using natural language. View and save your results, download the conversation log, or share it using the built-in export option.

LLM Benchmark Results for Gemma 1B/2B/4B/9B/12B/27B Hosting

Explore benchmark results for hosting Google's Gemma language models across various parameter sizes — from 1B to 27B. This report highlights key performance metrics such as inference speed (tokens per second), VRAM usage, and GPU compatibility across platforms like Ollama, vLLM, and Hugging Face Transformers. Understand how different GPU configurations (e.g., RTX 4090, A100, H100) handle Gemma models in real-world hosting scenarios, and make informed decisions for efficient LLM deployment at scale.

Ollama Benchmark for Gemma

This benchmark evaluates the performance of Google’s Gemma models (2B, 7B, etc.) running on the Ollama platform. It includes key metrics such as tokens per second, GPU memory usage, and startup latency across different hardware (e.g., RTX 4060, 4090, A100). Ollama's streamlined local deployment makes it easy to test and run Gemma models efficiently, even on consumer-grade GPUs. Ideal for developers seeking low-latency, private inference for chatbots, coding assistants, and research tools.

vLLM Benchmark for Gemma

This benchmark report showcases the performance of Google’s Gemma models (e.g., 2B, 7B) running on the vLLM inference engine — optimized for throughput and scalability. It includes detailed metrics such as tokens-per-second (TPS), GPU memory consumption, and latency across various hardware (like A100, H100, RTX 4090). vLLM's continuous batching and paged attention enable Gemma to serve multiple concurrent requests efficiently, making it a powerful choice for production-grade LLM APIs, assistants, and enterprise workloads.

How to Deploy Gemma LLMs with Ollama/vLLM

Install and Run Gemma Locally with Ollama >

Ollama is a self-hosted AI solution to run open-source large language models, such as DeepSeek, Gemma, Llama, Mistral, and other LLMs locally or on your own infrastructure.

Install and Run Gemma Locally with vLLM v1 >

vLLM is an optimized framework designed for high-performance inference of Large Language Models (LLMs). It focuses on fast, cost-efficient, and scalable serving of LLMs.

What Does Gemma Hosting Stack Include?

Hardware Stack

✅ GPU: NVIDIA RTX 3060 / T4 / 4060 (8–12 GB VRAM), NVIDIA RTX 4090 / A100 / H100 (24–80 GB VRAM)

✅ CPU: 4+ cores (Intel/AMD)

✅ RAM: 16–32 GB

✅ Storage: SSD, 50–100 GB free (for model files and logs)

✅ Networking: 1 Gbps for API access (if remote)

✅ Power & Cooling: Efficient PSU & cooling system, Required for stable GPU performance

Software Stack

✅ OS: Ubuntu 20.04 / 22.04 LTS(preferred), or other Linux distros

✅ Driver & CUDA: NVIDIA GPU Drivers + CUDA 11.8+ (depends on inference engine)

✅ Model Runtime: Ollama/vLLM/ Hugging Face Transformers/Text Generation Inference (TGI)

✅ Model Format: Gemma FP16 / INT4 / GGUF (depending on use case and platform)

✅ Containerization: Docker + NVIDIA Container Toolkit (optional but recommended for deployment)

✅ API Framework: FastAPI, Flask, or Node.js-based backend for serving LLM endpoints

✅ Monitoring: Prometheus + Grafana, or basic logging tools

✅ Optional Tools: Nginx (reverse proxy), Redis (cache), JWT/Auth layer for production deployment

Why Gemma Hosting Needs a GPU Hardware + Software Stack

Gemma Models Are GPU-Accelerated by Design

Google’s Gemma models (e.g., 4B, 12B, 27B) are designed to run efficiently on GPUs. These models involve billions of parameters and perform matrix-heavy computations—tasks that CPUs handle slowly and inefficiently. GPUs (like NVIDIA A100, H100, or even RTX 4090) offer thousands of cores optimized for parallel processing, enabling fast inference and training.

Inference Speed and Latency Optimization

Whether you're serving an API, chatbot, or batch processing tool, low-latency response is critical. A properly tuned GPU setup with frameworks like vLLM, Ollama, or Hugging Face Transformers allows you to serve multiple concurrent users with sub-second latency, which is almost impossible to achieve with CPU-only setups.

High Memory and Efficient Software Stack Required

Gemma models often require 8–80 GB of GPU VRAM, depending on their size and quantization format (FP16, INT4, etc.). Without enough VRAM and memory bandwidth, models will fail to load or run slowly.

Scalability and Production-Ready Deployment

To deploy Gemma models at scale—for use cases like LLM APIs, chatbots, or internal tools—you need an optimized environment. This includes load balancers, monitoring, auto-scaling infrastructure, and inference-optimized backends. Such production-level deployments rely heavily on GPU-enabled hardware and a carefully configured software stack to maintain uptime, performance, and reliability.

Self-hosted Gemma Hosting vs. Gemma as a Service

Feature	Self-hosted Gemma Hosting	Gemma as a Service (aaS)
Deployment Control	Full control over model, infra, scaling & updates	Limited — managed by provider
Customization	High — optimize models, quantization, backends	Low — predefined settings and APIs
Performance	Tuned for specific workloads (e.g. vLLM, TensorRT-LLM)	General-purpose, may include usage limits
Initial Cost	High — GPU server or cluster required	Low — pay-as-you-go pricing
Recurring Cost	Lower long-term for consistent usage	Can get expensive at scale or high usage
Latency	Lower (models run locally or in private cloud)	Higher due to shared/public infrastructure
Security & Compliance	Private data stays in your environment	Depends on provider’s data policies
Scalability	Manual or automated scaling with Kubernetes, etc.	Automatically scalable (but capped by plan)
DevOps Effort	High — setup, monitoring, updates	None — fully managed
Best For	Companies needing full control & optimization	Startups, small teams, quick prototyping

FAQs of Gemma 3/2 Service Hosting

What are Gemma Service, and who developed them?

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What are the typical use cases for hosting Gemma Service?

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Which inference engines are compatible with Gemma Service?

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Can Gemma Service be fine-tuned or customized?

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What are the benefits of self-hosting Gemma vs using it via API?

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Is Gemma available on Hugging Face for vLLM?

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