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gemma 2 9b it

Runs as your own dedicated GPU deployment, ready to launch from the wizard. Data stays in Europe.

gemma 2 9b it is an open-source language model from Google with 9.2B parameters, hosted on EU GPUs via an OpenAI-compatible API.

Start for free ← All models
google/gemma-2-9b-it vLLM ready
text->text · google · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
9.2B
Parameters
—
Context window
24GB
Minimum VRAM
POST /api/v1/chat/completions On request

Specifications

Parameters 9.2B
Minimum VRAM 24 GB
Architecture Gemma2ForCausalLM (vLLM)
License gemma
Modality text->text
Released June 2024
Publisher google ↗

Pricing

Shared router · per token
On request
Not available on the shared router. Pricing on request as a dedicated GPU deployment.
Dedicated GPU · per hour
from €1,08 per hour
Your own vLLM instance on European cloud (24 GB VRAM), billed hourly.

Shared EU router, pay-per-token, scale-to-zero. Dedicated GPU deployments are billed hourly, see pricing.

✓ Verified working on 23-07-2026, responded in 1139 ms on our EU infrastructure.

Bench index

Bench is our quality index for every model, 0 to 100. It rests on public measurements (Epoch AI with about 40 benchmarks, LiveBench, LMArena and more) and is refreshed every morning. Where nobody measures, it shows an estimate with a range.

37.3 / 100 · Measured

Measured by Epoch AI across 6 benchmarks, with a range.

Range 27.2 to 40.3 · Rank 196 of 270 at Epoch AI

Sources: Epoch AI ↗ · LiveBench ↗ · LMArena ↗ · Refreshed daily, last on 23-10-2026

LiveBench · 28.7
Reasoning 15.2
Coding 22.5
Mathematics 19.8
Data analysis 36.4
Language 25.5
Instruction following 52.6
Every benchmark (6)
GSM8K 84.9
PIQA 67.4
MMLU 62.8
MATH level 5 21.0
GPQA diamond 3.4
OTIS Mock AIME 2024-2025 0.5
LiveBench · All tasks
Reasoning
web of lies v2 20.0
zebra puzzle 11.5
spatial 14.0
Coding
LCB generation 26.9
coding completion 18.0
Mathematics
AMPS Hard 30.0
math comp 13.5
olympiad 15.9
Data analysis
cta 50.0
tablejoin 5.2
tablereformat 54.0
Language
connections 16.7
plot unscrambling 21.9
typos 38.0
Instruction following
paraphrase 52.1
simplify 61.0
story generation 51.6
summarize 45.8

Call it now

Drop-in replacement for OpenAI: change only the base URL and API key. The Anthropic format (/v1/messages) is supported too.

curl https://hostyourai.com/api/v1/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemma-2-9b-it",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run gemma 2 9b it in the EU?

Yes. HostYourAI runs gemma 2 9b it on GPUs in European datacenters via vLLM. Prompts and outputs never leave the EU and there is no US cloud provider in the chain.

Is hosting gemma 2 9b it GDPR-compliant?

Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open-source weights also mean: no training on your data.

How much does gemma 2 9b it cost?

gemma 2 9b it is available on request: through the shared EU router after a quick chat, or as your own dedicated GPU deployment billed per hour. Tell us your use case and we will set it up for you.

Is the API OpenAI-compatible?

Yes. You use the standard OpenAI SDKs with a custom base URL (https://hostyourai.com/api/v1). The Anthropic Messages API is supported as a drop-in as well.

More models from Google

embeddinggemma 2

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders.

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diffusiongemma 26B A4B it

DiffusionGemma is a generative model built by Google DeepMind. Based on the 26B A4B Mixture-of-Experts (MoE) Gemma 4 architecture, DiffusionGemma generates tokens using discrete diffusion. This open-weights model is multimodal, handling text, image, and video inputs to generate text output.

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gemma 4 31B it qat w4a16 ct

[!Note] This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. Four versions of the QAT checkpoints are available: Unquantized QAT checkpoints (Q40): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. GGUF (Q40): Ready-to-deploy formats for broad ecosystem compatibility. Availabl

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gemma 4 26B A4B it qat q4 0 unquantized

[!Note] This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. Four versions of the QAT checkpoints are available: Unquantized QAT checkpoints (Q40): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. GGUF (Q40): Ready-to-deploy formats for broad ecosystem compatibility. Availabl

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gemma 4 31B it qat q4 0 unquantized

[!Note] This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. Four versions of the QAT checkpoints are available: Unquantized QAT checkpoints (Q40): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. GGUF (Q40): Ready-to-deploy formats for broad ecosystem compatibility. Availabl

31B 262K context View model →
gemma 4 31B

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

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