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
There are no public endpoints for this model yet.

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 Qwen2.5-14B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
32,768.0
1 $0.53 1.520 Launch
teslat4-2.16.32.160
32,768.0
tensor
2 $0.54 2.303 Launch
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 2.303 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 1.520 Launch
rtx2080ti-3.12.24.120
32,768.0
pipeline
3 $0.84 2.037 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 1.520 Launch
rtx2080ti-4.16.32.160
32,768.0
tensor
4 $1.12 3.270 Launch
teslav100-1.12.64.160
32,768.0
1 $1.20 2.720 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 4.703 Launch
rtx3080-3.16.64.160
32,768.0
pipeline
3 $1.43 1.587 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 2.720 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 2.670 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 9.920 Launch
h100-1.16.64.160
32,768.0
1 $3.83 9.920 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 12.020 Launch
h200-1.16.128.160
32,768.0
1 $4.74 19.070 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
32,768.0
1 $0.53 1.010 Launch
teslat4-2.16.32.160
32,768.0
tensor
2 $0.54 1.794 Launch
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 1.794 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 1.010 Launch
rtx2080ti-3.12.24.120
32,768.0
pipeline
3 $0.84 1.527 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 1.010 Launch
rtx2080ti-4.16.32.160
32,768.0
tensor
4 $1.12 2.760 Launch
teslav100-1.12.64.160
32,768.0
1 $1.20 2.210 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 4.194 Launch
rtx3080-3.16.64.160
32,768.0
pipeline
3 $1.43 1.077 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 2.210 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 2.160 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 9.410 Launch
h100-1.16.64.160
32,768.0
1 $3.83 9.410 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 11.510 Launch
h200-1.16.128.160
32,768.0
1 $4.74 18.560 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
32,768.0
pipeline
3 $0.88 1.022 Launch
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 1.438 Launch
teslat4-4.16.64.160
32,768.0
tensor
4 $0.96 3.005 Launch
teslaa2-3.32.128.160
32,768.0
pipeline
3 $1.06 1.022 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 1.438 Launch
teslaa2-4.32.128.160
32,768.0
tensor
4 $1.26 3.005 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 1.438 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 1.438 Launch
teslav100-2.16.64.240
32,768.0
tensor
2 $2.22 3.838 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 6.655 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 3.838 Launch
h100-1.16.64.160
32,768.0
1 $3.83 6.655 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 8.755 Launch
h200-1.16.128.160
32,768.0
1 $4.74 15.805 Launch

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