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Model garden Router · warm Dedicated · available

gemma 3 27b it

Instantly via the EU router or as a dedicated GPU deployment. Data stays in Europe.

gemma 3 27b it is an multimodal language model from Google with 27B parameters and a 110K-token context window, hosted on EU GPUs via an OpenAI-compatible API.

Start for free ← All models
google/gemma-3-27b-it vLLM ready
text+image->text · google · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
27B
Parameters
110K
Context window
80GB
Minimum VRAM
POST /api/v1/chat/completions 200 OK

Specifications

Parameters 27B
Context window 110,000 tokens
Minimum VRAM 80 GB
Architecture Gemma3ForConditionalGeneration (vLLM)
License gemma
Modality text+image->text
Released March 2025
Publisher google ↗

Pricing

Shared router · per token
€0.10
Input (per 1M tokens)
€0.32
Output (per 1M tokens)
Dedicated GPU · per hour
from €1,08 per hour
Your own vLLM instance on European cloud (80 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 985 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.

50.0 / 100 · Measured

Measured by Epoch AI across 9 benchmarks, with a range.

Range 43.1 to 52.5 · Rank 160 of 270 at Epoch AI

Sources: Epoch AI ↗ · LiveBench ↗ · LMArena ↗ · Refreshed daily, last on 24-10-2026

LiveBench · 48.4
Reasoning 34.4
Coding 48.9
Mathematics 52.3
Data analysis 38.8
Language 41.3
Instruction following 74.9
Every benchmark (9)
Lech Mazur Writing 79.9
MATH level 5 74.0
GeoBench 52.0
Fiction.LiveBench 33.3
GPQA diamond 31.6
OTIS Mock AIME 2024-2025 22.4
DTBench 20.9
LMCA 14.4
Aider polyglot 4.9
LiveBench · All tasks
Reasoning
web of lies v3 38.0
zebra puzzle 29.3
spatial 36.0
Coding
code generation 47.9
code completion 50.0
Mathematics
AMPS Hard 74.0
math comp 55.2
olympiad 27.6
Data analysis
tablejoin 18.8
tablereformat 58.8
Language
connections 27.3
plot unscrambling 28.6
typos 68.0
Instruction following
paraphrase 71.5
simplify 77.5
story generation 70.6
summarize 80.0

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-3-27b-it",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run gemma 3 27b it in the EU?

Yes. HostYourAI runs gemma 3 27b 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 3 27b 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 3 27b it cost?

Via the shared EU router you pay €0.10 per million input tokens and €0.32 per million output tokens, with no fixed costs. For high volume or isolation you can also run gemma 3 27b it as a dedicated hourly GPU instance.

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.

0.7B 262K context View model →
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.

26B 262K context View model →
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

33B 262K context View model →
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

26B 262K context View model →
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.

33B 262K context View model →

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