Gemma-3-27B

multimodal

Gemma 3 27B is the flagship multimodal model in Google DeepMind's Gemma 3 family, designed to tackle the most complex challenges in artificial intelligence. With 27 billion parameters and a context window of up to 128K tokens, the model combines high performance with computational efficiency, delivering state-of-the-art results in both text-based and multimodal tasks.

The model is built upon an architecture inherited from Gemini 2.0 and employs advanced distillation techniques along with an optimized attention structure. This allows it to run even on a single GPU while maintaining performance comparable to models that require up to 32 GPUs. Thanks to its extended context window, SigLIP encoder, and "Pan&Scan" technology, the model can process long documents, scientific papers, and images within a single query without losing coherence or response quality. This makes it particularly valuable for legal and technical analysis, medical diagnostics, and content research involving visual elements. Gemma 3 27B supports more than 140 languages, and an improved tokenizer ensures high-quality translation and cross-lingual analysis.

Gemma 3 27B is not just another language model—it is a next-generation AI assistant that combines the power of large-scale models with accessibility and flexibility. Among its peers, it stands out due to its unique combination of efficiency, multimodality, multilingual support, and high performance on a single graphics card.


Announce Date: 12.03.2025
Parameters: 27B
Context: 132K
Layers: 62, using full attention: 10
Attention Type: Sliding Window Attention
Developer: Google DeepMind
Transformers Version: 4.50.0.dev0
License: gemma

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Gemma-3-27B capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU TPS Status Link
RedHatAI/gemma-3-27b-it-quantized.w4a16 128,000.0 Public 2×RTX4090
tensor
AVAILABLE chat

API access to Gemma-3-27B endpoints

curl https://chat.immers.cloud/v1/endpoints/gemma3-27b-int4/generate/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer USER_API_KEY" \
-d '{"model": "Gemma-3-27b-it", "messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"}
], "temperature": 0, "max_tokens": 150}'
$response = Invoke-WebRequest https://chat.immers.cloud/v1/endpoints/gemma3-27b-int4/generate/chat/completions `
-Method POST `
-Headers @{
"Authorization" = "Bearer USER_API_KEY"
"Content-Type" = "application/json"
} `
-Body (@{
model = "Gemma-3-27b-it"
messages = @(
@{ role = "system"; content = "You are a helpful assistant." },
@{ role = "user"; content = "Say this is a test" }
)
} | ConvertTo-Json)
($response.Content | ConvertFrom-Json).choices[0].message.content
#!pip install OpenAI --upgrade

from openai import OpenAI

client = OpenAI(
api_key="USER_API_KEY",
base_url="https://chat.immers.cloud/v1/endpoints/gemma3-27b-int4/generate/",
)

chat_response = client.chat.completions.create(
model="Gemma-3-27b-it",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"},
]
)
print(chat_response.choices[0].message.content)

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended server configurations for hosting Gemma-3-27B

Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
131,072.0
pipeline
3 $0.88 1.172 Launch
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 39.820 1.355 Launch
teslat4-4.16.64.160
131,072.0
tensor
4 $0.96 2.043 Launch
teslaa2-3.32.128.160
131,072.0
pipeline
3 $1.06 1.172 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 44.970 1.355 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 2.043 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 38.370 1.355 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 52.790 1.355 Launch
teslav100-2.16.64.240
131,072.0
tensor
2 $2.22 2.409 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 51.560 3.647 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 53.100 2.409 Launch
h100-1.16.64.160
131,072.0
1 $3.83 49.870 3.647 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 63.370 4.569 Launch
h200-1.16.128.160
131,072.0
1 $4.74 7.667 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-4.16.64.160
131,072.0
tensor
4 $0.96 1.644 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 1.644 Launch
teslaa10-3.16.96.160
131,072.0
pipeline
3 $1.34 2.355 Launch
teslaa10-4.12.48.160
131,072.0
tensor
4 $1.57 3.753 Launch
teslav100-2.16.64.240
131,072.0
tensor
2 $2.22 2.010 Launch
rtx3090-3.16.96.160
131,072.0
pipeline
3 $2.29 2.355 Launch
rtxa5000-4.16.128.160.nvlink
131,072.0
tensor
4 $2.34 3.753 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 3.248 Launch
rtx4090-3.16.96.160
131,072.0
pipeline
3 $2.83 2.355 Launch
rtx3090-4.16.64.160
131,072.0
tensor
4 $2.89 3.753 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 2.010 Launch
rtx4090-4.16.64.160
131,072.0
tensor
4 $3.60 3.753 Launch
h100-1.16.64.160
131,072.0
1 $3.83 3.248 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 4.171 Launch
h200-1.16.128.160
131,072.0
1 $4.74 7.268 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-6.32.128.160
131,072.0
pipeline
6 $1.65 1.249 Launch
teslaa10-4.16.128.160
131,072.0
tensor
4 $1.75 1.615 Launch
rtxa5000-4.16.128.160.nvlink
131,072.0
tensor
4 $2.34 1.615 Launch
teslaa100-1.16.128.160
131,072.0
1 $2.50 23.640 1.109 Launch
rtx3090-4.16.96.320
131,072.0
tensor
4 $2.97 29.890 1.615 Launch
rtx4090-4.16.96.320
131,072.0
tensor
4 $3.68 40.920 1.615 Launch
teslav100-3.64.256.320
131,072.0
pipeline
3 $3.89 1.798 Launch
h100-1.16.128.160
131,072.0
1 $3.95 26.520 1.109 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 2.032 Launch
rtx5090-3.16.96.160
131,072.0
pipeline
3 $4.34 1.798 Launch
teslav100-4.32.96.160
131,072.0
tensor
4 $4.35 3.724 Launch
h200-1.16.128.160
131,072.0
1 $4.74 5.130 Launch
rtx5090-4.16.128.160
131,072.0
tensor
4 $5.74 3.724 Launch

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