QwQ-32B is an innovative language model developed by Alibaba, featuring 32 billion parameters and a context window of 131K tokens. It is specifically designed for deep reasoning and excels at multi-step logical analysis, making it highly effective in solving complex tasks that require structured thinking.
QwQ-32B was trained using advanced reinforcement learning techniques, significantly enhancing its reasoning capabilities. This enables the model to deliver outstanding performance in areas such as mathematical computation, programming, and legal document analysis. In terms of performance, it rivals DeepSeek-R1, which has 671 billion parameters. Additionally, QwQ-32B possesses agent-like behavior capabilities, allowing it to adapt its reasoning based on feedback and utilize various tools for more accurate query analysis.
Thanks to its context window of 131,000 tokens, the model can handle large-scale analytical tasks and work with multi-step logical reasoning chains. This makes it indispensable for scientific research, educational applications, identifying issues in code, comparing arguments in legal documents, and other tasks that demand maximum attention to detail.
| Model Name | Context | Type | GPU | TPS | Status | Link |
|---|---|---|---|---|---|---|
| Qwen/QwQ-32B | 40,960.0 | Public | 2×RTX4090 | 46.40 | AVAILABLE | try |
curl https://chat.immers.cloud/v1/endpoints/QwQ-32b/generate/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer USER_API_KEY" \
-d '{"model": "QwQ-32b", "messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"}
], "temperature": 0, "max_tokens": 150}'
$response = Invoke-WebRequest https://chat.immers.cloud/v1/endpoints/QwQ-32b/generate/chat/completions `
-Method POST `
-Headers @{
"Authorization" = "Bearer USER_API_KEY"
"Content-Type" = "application/json"
} `
-Body (@{
model = "QwQ-32b"
messages = @(
@{ role = "system"; content = "You are a helpful assistant." },
@{ role = "user"; content = "Say this is a test" }
)
} | ConvertTo-Json)
($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/QwQ-32b/generate/",
)
chat_response = client.chat.completions.create(
model="QwQ-32b",
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 | |||
|---|---|---|---|---|---|---|---|
| 16 | 98304 | 160 | 3 | $1.34 | Launch | ||
| 16 | 65536 | 160 | 4 | $1.48 | Launch | ||
| 16 | 98304 | 160 | 3 | $2.45 | Launch | ||
| 16 | 65536 | 160 | 1 | $2.58 | Launch | ||
| 16 | 65536 | 160 | 2 | $2.93 | Launch | ||
| 16 | 98304 | 160 | 3 | $3.23 | Launch | ||
| 16 | 65536 | 160 | 1 | $5.11 | Launch | ||
| Name | vCPU | RAM, MB | Disk, GB | GPU | |||
|---|---|---|---|---|---|---|---|
| 16 | 98304 | 160 | 3 | $1.34 | Launch | ||
| 16 | 98304 | 160 | 3 | $2.45 | Launch | ||
| 16 | 131072 | 160 | 1 | $2.71 | Launch | ||
| 16 | 98304 | 160 | 3 | $3.23 | Launch | ||
| 16 | 98304 | 160 | 3 | $4.34 | Launch | ||
| 16 | 131072 | 160 | 1 | $5.23 | Launch | ||
Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.