The Llama 4 Maverick is a versatile model in the next generation of the Llama 4 family, released in April 2025. Unlike the more compact Scout, the Maverick is designed for those who need to maximize performance and processing power. The model uses MoE architecture with 128 experts, which has 400 billion parameters in total - making it one of the largest scale open source models on the market. However, only 17 billion parameters are active at each step, significantly reducing inference latency and resource requirements. Like all models in the Maverik series, Maverik supports native multimodality, making it easy to input and process images together with image support text.
With its large-scale architecture, Maverick offers consistency and depth of reasoning, accuracy and security of responses. The model performs respectably on complex logical reasoning, math, and programming tasks, outperforming previous generations of LLMs and major competitors on several key metrics.
The model incorporates advanced security features such as Llama Guard 4 (12B) to filter unsafe content and Llama Prompt Guard 2 (22M and 86M) to protect against manipulative or malicious input.
Llama 4 Maverick is ideal for complex enterprise solutions and large-scale projects where deep insight and precise execution are required.
| Model Name | Context | Type | GPU | TPS | Status | Link |
|---|---|---|---|---|---|---|
| RedHatAI/Llama-4-Maverick-17B-128E-Instruct-quantized.w4a16 | 250,000.0 | Public | 4×TeslaA100 | 37.97 | AVAILABLE | try |
curl https://chat.immers.cloud/v1/endpoints/Llama-4-Maverick-2025-21-07/generate/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer USER_API_KEY" \
-d '{"model": "Llama-4-Maverick-17B-128E-Instruct", "messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"}
], "temperature": 0, "max_tokens": 150}'
$response = Invoke-WebRequest https://chat.immers.cloud/v1/endpoints/Llama-4-Maverick-2025-21-07/generate/chat/completions `
-Method POST `
-Headers @{
"Authorization" = "Bearer USER_API_KEY"
"Content-Type" = "application/json"
} `
-Body (@{
model = "Llama-4-Maverick-17B-128E-Instruct"
messages = @(
@{ role = "system"; content = "You are a helpful assistant." },
@{ role = "user"; content = "Say this is a test" }
)
} | ConvertTo-Json)
($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/Llama-4-Maverick-2025-21-07/generate/",
)
chat_response = client.chat.completions.create(
model="Llama-4-Maverick-17B-128E-Instruct",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"},
]
)
print(chat_response.choices[0].message.content)
Rent your own physically dedicated instance with hourly or long-term monthly billing.
We recommend deploying private instances in the following scenarios:
| Name | vCPU | RAM, MB | Disk, GB | GPU | |||
|---|---|---|---|---|---|---|---|
| 32 | 393216 | 320 | 3 | $8.01 | Launch | ||
| 32 | 393216 | 320 | 3 | $15.58 | Launch | ||
| Name | vCPU | RAM, MB | Disk, GB | GPU |
|---|
| Name | vCPU | RAM, MB | Disk, GB | GPU |
|---|
Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.