Qwen2.5-14B-Instruct

Qwen2.5-14B features 14 billion parameters, 48 layers, and a 40/8 attention head architecture, representing a substantial increase in computational power and complexity compared to the 7B version. The model supports a 128K-token context window with 8K-token generation capability, enabling it to process voluminous documents and execute complex multi-step tasks.

The uniqueness of Qwen2.5-14B lies in its reintroduction to the series after being absent from Qwen2, effectively bridging the critical gap between 7B and larger models. This size proves particularly valuable for organizations requiring high performance without the substantial costs associated with 32B or 72B-level models. The model demonstrates significant improvements in expert-level knowledge, complex reasoning, and multi-domain task handling capabilities.

Qwen2.5-14B is ideally suited for medium-to-large scale enterprise applications demanding high-quality processing with reasonable infrastructure costs. The model excels in knowledge management systems, comprehensive analytics, and serves as an excellent foundation for developing industry-specific AI solutions.


Announce Date: 16.09.2024
Parameters: 14B
Context: 33K
Layers: 48
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.43.1
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2.5-14B-Instruct capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to Qwen2.5-14B-Instruct endpoints

There are no public endpoints for this model yet.

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 Qwen2.5-14B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
32,768.0
1 $0.53 49.96 1.331 Launch
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 25.47 1.788 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 81.00 1.493 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 98.50 1.648 Launch
rtx3080-3.16.64.160
32,768.0
pipeline
3 $1.43 62.62 1.161 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 4.536 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 2.688 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 2.065 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 118.20 10.033 Launch
h100-1.16.64.160
32,768.0
1 $3.83 154.67 10.022 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 12.155 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 21.614 Launch
h200-1.16.128.160
32,768.0
1 $4.74 19.317 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 40.183 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 19.10 1.334 Launch
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 52.06 3.750 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 72.52 4.088 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 4.088 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 1.934 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 82.54 4.056 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 76.57 9.152 Launch
h100-1.16.64.160
32,768.0
1 $3.83 76.17 9.089 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 109.54 11.220 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 21.153 Launch
h200-1.16.128.160
32,768.0
1 $4.74 18.563 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 39.722 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 1.710 Launch
teslaa2-3.32.128.160
32,768.0
pipeline
3 $1.06 1.253 Launch
teslaa2-4.32.128.160
32,768.0
tensor
4 $1.26 3.218 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 46.96 2.055 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 2.055 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 55.72 2.023 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 48.01 7.235 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 4.424 Launch
h100-1.16.64.160
32,768.0
1 $3.83 56.95 7.224 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 9.357 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 19.113 Launch
h200-1.16.128.160
32,768.0
1 $4.74 16.519 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 37.682 Launch

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