gemma-4-26B-A4B-it

reasoning
multimodal
coding

Gemma‑4‑26B‑A4B‑it is Google’s first open model based on the Mixture‑of‑Experts (MoE) architecture. With a total of 25.2 billion parameters, only a small fraction — between 3.8 and 4 billion — is activated for each token. According to the developers, this efficiency allows the model to achieve approximately 97% of the quality of the dense 31B model at significantly lower computational cost. At release, the model ranks 6th on the Arena AI leaderboard among open models, outperforming competitors that are 20 times larger.

The 26B A4B model is built on 30 layers and uses hybrid attention with a sliding window of 1024 tokens, supporting a context window of 256 thousand tokens. It has multimodal capabilities, handling both text and images exceptionally well. Unlike dense alternatives, the MoE model is specifically optimised for efficient execution of agentic workflows, demonstrating significant progress over Gemma‑3. 

For developers, the key advantage of this model is its exceptional deployment efficiency. Community estimates indicate that the model can generate 162 tokens per second on an NVIDIA RTX 4090 accelerator and can run effectively even on memory‑constrained devices. This makes it an ideal choice for complex agentic systems, deep code analysis, and intensive reasoning tasks where a balance between performance and hardware costs is required.

For the developers’ usage recommendations for the model, please refer to this link: https://ai.google.dev/gemma/docs/core/model_card_4?hl=en


Announce Date: 11.03.2026
Parameters: 27B
Experts: 128
Activated at inference: 4B
Context: 263K
Layers: 30, using full attention: 5
Attention Type: Sliding Window Attention
Developer: Google DeepMind
Transformers Version: 5.5.0.dev0
vLLM Version: gemma4
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore gemma-4-26B-A4B-it capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU TPS Tooling Status Link
google/gemma-4-26B-A4B-it 263K Public 4×4090
tensor
238.0 yes AVAILABLE chat

API access to gemma-4-26B-A4B-it endpoints

curl https://chat.immers.cloud/v1/endpoints/generate/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer USER_API_KEY" \
--data-binary @- <<"EOF"
{"model": "gemma-4-26b-a4b-it", "messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"}
], "temperature": 0, "max_tokens": 150
}
EOF
 
$response = Invoke-WebRequest https://chat.immers.cloud/v1/endpoints/generate/chat/completions `
-Method POST `
-Headers @{
"Authorization" = "Bearer USER_API_KEY"
"Content-Type" = "application/json; charset=utf-8"
} `
-Body ([System.Text.Encoding]::UTF8.GetBytes((@{
model = "gemma-4-26b-a4b-it"
messages = @(
@{ role = "system"; content = "You are a helpful assistant." },
@{ role = "user"; content = "Say this is a test" })
} | ConvertTo-Json -Depth 10)))
([System.Text.Encoding]::UTF8.GetString($response.RawContentStream.ToArray()) | 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/generate/",
)

chat_response = client.chat.completions.create(
model="gemma-4-26b-a4b-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)
 
# linux
export ANTHROPIC_BASE_URL=https://chat.immers.cloud/v1/endpoints/generate/
export ANTHROPIC_API_KEY=USER_API_KEY
export ANTHROPIC_MODEL=gemma-4-26b-a4b-it

# windows (ps)
[Environment]::SetEnvironmentVariable("ANTHROPIC_BASE_URL", "https://chat.immers.cloud/v1/endpoints/generate/", "User")
[Environment]::SetEnvironmentVariable("ANTHROPIC_AUTH_TOKEN", "USER_API_KEY", "User")
[Environment]::SetEnvironmentVariable("ANTHROPIC_MODEL", "gemma-4-26b-a4b-it", "User")
 
# linux
export OPENAI_BASE_URL=https://chat.immers.cloud/v1/endpoints/generate/
export OPENAI_API_KEY=USER_API_KEY
export OPENAI_MODEL=gemma-4-26b-a4b-it

# windows (ps)
[Environment]::SetEnvironmentVariable("OPENAI_BASE_URL", "https://chat.immers.cloud/v1/endpoints/generate/", "User")
[Environment]::SetEnvironmentVariable("OPENAI_API_KEY", "USER_API_KEY", "User")
[Environment]::SetEnvironmentVariable("OPENAI_MODEL", "gemma-4-26b-a4b-it", "User")
 

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint gemma-4-26B-A4B-it

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
262,144.0
tensor
2 $0.93 3.361 Launch
teslaa2-3.32.128.160
262,144.0
pipeline
3 $1.06 2.352 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 2.805 Launch
rtx3090-2.16.64.160
262,144.0
tensor
2 $1.56 168.50 3.411 Launch
rtx3090-2.16.64.160.nvlink
262,144.0
tensor
2 $1.56 84.00 3.411 Launch
rtx4090-2.16.64.160
262,144.0
tensor
2 $1.92 249.00 3.455 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 122.66 8.627 Launch
h100-1.16.64.160
262,144.0
1 $3.83 6.366 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 7.904 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 166.86 19.947 Launch
h200-1.16.128.160
262,144.0
1 $4.74 13.069 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 28.424 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.417 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 3.181 Launch
rtx3090-2.16.64.160
262,144.0
tensor
2 $1.56 141.00 1.580 Launch
rtx3090-2.16.64.160.nvlink
262,144.0
tensor
2 $1.56 1.580 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 4.025 Launch
rtx4090-2.16.64.160
262,144.0
tensor
2 $1.92 163.33 1.795 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 114.60 7.123 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 3.575 Launch
h100-1.16.64.160
262,144.0
1 $3.83 132.83 4.912 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 209.64 6.809 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 17.912 Launch
h200-1.16.128.160
262,144.0
1 $4.74 11.300 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 26.625 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.64.160
262,144.0
tensor
4 $1.62 2.341 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 2.051 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.014 Launch
teslaa100-1.16.64.160
262,144.0
1 $2.37 100.14 3.629 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.290 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 199.00 2.438 Launch
rtx3090-4.16.128.160.nvlink
262,144.0
tensor
4 $3.01 2.438 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 238.00 2.536 Launch
h100-1.16.64.160
262,144.0
1 $3.83 2.606 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 171.47 4.314 Launch
rtx5090-3.16.96.160
262,144.0
pipeline
3 $4.34 4.009 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 15.161 Launch
h200-1.16.128.160
262,144.0
1 $4.74 9.379 Launch
rtx5090-4.16.128.160
262,144.0
tensor
4 $5.74 5.263 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 24.592 Launch

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