Gemma Hosting

Gemma Hosting — Deploy Gemma 3/2 Models with Ollama, vLLM, TGI, TensorRT-LLM & GGML

 

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 models securely and efficiently—tailored for developers, enterprises, and innovators.

 

                Gemma Hosting with Ollama — GPU Recommendation

Deploying DeepSeek models using Ollama is a flexible and developer-friendly way to run powerful LLMs locally or on servers. However, choosing the right GPU is critical to ensure smooth performance and fast inference, especially as model sizes scale from lightweight 1.5B to massive 70B+ parameters.
Model NameSize (4-bit Quantization)Recommended GPUsTokens/s
gemma3:1b815MBP1000 < GTX1650 < GTX1660 < RTX206028.90-43.12
gemma2:2b1.6GBP1000 < GTX1650 < GTX1660 < RTX206019.46-38.42
gemma3:4b3.3GBGTX1650 < GTX1660 < RTX2060 < T1000 < RTX3060 Ti < RTX4060 < RTX506028.36-80.96
gemma2:9b5.4GBT1000 < RTX3060 Ti < RTX4060 < RTX506012.83-21.35
gemma3n:e2b5.6GBT1000 < RTX3060 Ti < RTX4060 < RTX506030.26-56.36
gemma3n:e4b7.5GBA4000 < A5000 < V100 < RTX409038.46-70.90
gemma3:12b8.1GBA4000 < A5000 < V100 < RTX409030.01-67.92
gemma2:27b16GBA5000 < A6000 < RTX4090 < A100-40gb < H100 = RTX509028.79-47.33
gemma3:27b17GBA5000 < RTX4090 < A100-40gb < H100 = RTX509028.79-47.33

Gemma Hosting with vLLM + Hugging Face — GPU Recommendation

Host and deploy Google’s Gemma models 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 NameSize (16-bit Quantization)Recommended GPU(s)Concurrent RequestsTokens/s
google/gemma-3n-E4B-it
google/gemma-3-4b-it8.1GBA4000 < A5000 < V100 < RTX4090502014.88-7214.10
google/gemma-2-9b-it18GBA5000 < A6000 < RTX409050951.23-1663.13
google/gemma-3-12b-it
google/gemma-3-12b-it-qat-q4_0-gguf23GBA100-40gb < 2*A100-40gb< H10050477.49-4193.44
google/gemma-2-27b-it
google/gemma-3-27b-it
google/gemma-3-27b-it-qat-q4_0-gguf51GB2*A100-40gb < A100-80gb < H100501231.99-1990.61

Express GPU Dedicated Server - P1000

Best For College Project

$74/mo
    • 32 GB RAM
    • GPU: Nvidia Quadro P1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - T1000

For business

$109/mo
    • 64 GB RAM
    • GPU: Nvidia Quadro T1000
    • Eight-Core Xeon E5-2690
    • 120GB + 960GB SSD
    • 100Mbps-1Gbps
    • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1650

For business

$129/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1650
  • Eight-Core Xeon E5-2667v3
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - GTX 1660

For business

$149/mo
  • 64GB RAM
  • GPU: Nvidia GeForce GTX 1660
  • Dual 10-Core Xeon E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - V100

Best For College Project

$239/mo
  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU Dedicated Server - RTX 2060

For business

$209/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 10-Core E5-2660v2
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - RTX 2060

For business

$249/mo
  • 128GB RAM
  • GPU: Nvidia GeForce RTX 2060
  • Dual 20-Core Gold 6148
  • 120GB + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

 

Advanced GPU Dedicated Server - RTX 3060 Ti

For business

$249/mo
  • 128GB RAM
  • GPU: GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Professional GPU VPS - A4000

For Business

$139/mo
  • 32GB RAM
  • 24 CPU Cores
  • 320GB SSD
  • 300Mbps Unmetered Bandwidth
  • Once per 2 Weeks Backup
  • OS: Linux / Windows 10/ Windows 11

Advanced GPU Dedicated Server - A4000

For business

$289/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A4000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Advanced GPU Dedicated Server - A5000

For business

$279/mo
  • 128GB RAM
  • GPU: Nvidia Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A40

For business

$449/mo
  • 256GB RAM
  • GPU: Nvidia A40
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Basic GPU Dedicated Server - RTX 5060

For Business

$199/mo
  • 64GB RAM
  • GPU: Nvidia GeForce RTX 5060
  • 24-Core Platinum 8160
  • 120GB SSD + 960GB SSD
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - RTX 5090

For business

$489/mo
  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100

For business

$809/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - A100(80GB)

For business

$1569/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Enterprise GPU Dedicated Server - H100

For Business

$2109/mo
  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 4090

For business

$739/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server- 2xRTX 5090

For business

$869/mo
  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual Gold 6148
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xA100

For business

$1309/mo
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 3060 Ti

For Business

$329/mo
  • 128GB RAM
  • GPU: 2 x GeForce RTX 3060 Ti
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX 4060

For business

$279/mo
  • 64GB RAM
  • GPU: 2 x Nvidia GeForce RTX 4060
  • Eight-Core E5-2690
  • 120GB SSD + 960GB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A5000

For business

$449/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 2xRTX A4000

For business

$369/mo
  • 128GB RAM
  • GPU: 2 x Quadro RTX A5000
  • Dual 12-Core E5-2697v2
  • 240GB SSD + 2TB SSD
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX 3060 Ti

For Business

$379/mo
  • 256GB RAM
  • GPU: 3 x GeForce RTX 3060 Ti
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xV100

For business

$479/mo
  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A5000

For business

$549/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A5000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 3xRTX A6000

For business

$909/mo
  • 256GB RAM
  • GPU: 3 x Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xA100

For Business

$1909/mo
  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 4xRTX A6000

For business

$1209/mo
  • 512GB RAM
  • GPU: 4 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xV100

For business

$1509/mo
  • 512GB RAM
  • GPU: 8 x Nvidia Tesla V100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux

Multi-GPU Dedicated Server - 8xRTX A6000

For business

$2109/mo
  • 512GB RAM
  • GPU: 8 x Quadro RTX A6000
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
gemma-1

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.

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
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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
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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?

    llama-2

    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

    gemma

    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

    FeatureSelf-hosted Gemma HostingGemma as a Service (aaS)
    Deployment ControlFull control over model, infra, scaling & updatesLimited — managed by provider
    CustomizationHigh — optimize models, quantization, backendsLow — predefined settings and APIs
    PerformanceTuned for specific workloads (e.g. vLLM, TensorRT-LLM)General-purpose, may include usage limits
    Initial CostHigh — GPU server or cluster requiredLow — pay-as-you-go pricing
    Recurring CostLower long-term for consistent usageCan get expensive at scale or high usage
    LatencyLower (models run locally or in private cloud)Higher due to shared/public infrastructure
    Security & CompliancePrivate data stays in your environmentDepends on provider’s data policies
    ScalabilityManual or automated scaling with Kubernetes, etc.Automatically scalable (but capped by plan)
    DevOps EffortHigh — setup, monitoring, updatesNone — fully managed
    Best ForCompanies needing full control & optimizationStartups, small teams, quick prototyping

    FAQs of Gemma 3/2 Models Hosting

    What are Gemma models, and who developed them?

    Gemma is a family of open-weight language models developed by Google DeepMind, optimized for fast and efficient deployment. They are similar in architecture to Google’s Gemini and include variants like Gemma-3 1B, 4B, 12B, and 27B.

    What are the typical use cases for hosting Gemma models?

    Gemma models are well-suited for:

    • Chatbots and conversational agents
    • Text summarization, Q&A, and content generation
    • Fine-tuning on domain-specific data
    • Academic or commercial NLP research
    • On-premises privacy-compliant LLM applications

    Which inference engines are compatible with Gemma models?

    You can deploy Gemma models using:

    • vLLM (optimized for high-throughput inference)
    • Ollama (easy local serving with model quantization)
    • TensorRT-LLM (for performance on NVIDIA GPUs)
    • Hugging Face Transformers + Accelerate
    • Text Generation Inference (TGI)

    Can Gemma models be fine-tuned or customized?

    Yes. Gemma supports LoRA fine-tuning and full fine-tuning, making it a good choice for domain-specific LLMs. You can use tools like PEFT, Hugging Face Transformers, or Axolotl for training.

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

    Self-hosting provides:

    • Better data privacy
    • Customization flexibility
    • Lower cost at scale
    • Lower latency (for edge or private deployment)

    However, APIs are easier to get started with and require no infrastructure.

    Is Gemma available on Hugging Face for vLLM?

    Yes. Most Gemma 3 models (1B, 4B, 12B, 27B) are available on Hugging Face and can be loaded into vLLM using 16-bit quantization.