Qwen2.5-72B-Instruct

Qwen2.5-72B is the flagship open-weight model of the series, featuring 72 billion parameters, 80 layers, and a 64/8 attention head architecture, representing the pinnacle of Alibaba's open-source language model capabilities. The model supports a 128K-token context window with 8K-token generation, enabling it to analyze multiple documents and produce detailed content with exceptional accuracy.

Trained on an extended dataset of 18 trillion tokens with enhanced filtering and specialized data in mathematics and programming, Qwen2.5-72B delivers outstanding performance across a wide range of tasks. Its most remarkable feature is achieving state-of-the-art results among open-weight models while being significantly smaller than competitors. According to the technical report, the model demonstrates performance competitive with Llama-3-405B-Instruct, despite being five times smaller in size.

Distributed under the special Qwen Research License, Qwen2.5-72B is designed for projects requiring the highest quality natural language processing. The model is ideally suited for: fundamental AI research, development of cutting-edge AI products, training and fine-tuning specialized models, serving as a foundation for multimodal systems and building advanced AI agents


Announce Date: 16.09.2024
Parameters: 73B
Context: 33K
Layers: 80
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.43.1
License: qwen

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2.5-72B-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 Qwen2.5-72B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-3.16.96.160
32,768.0
pipeline
3 $1.34 1.334 Launch
teslaa10-4.16.64.160
32,768.0
tensor
4 $1.62 3.122 Launch
teslaa2-6.32.128.160
32,768.0
pipeline
6 $1.65 2.049 Launch
rtx3090-3.16.96.160
32,768.0
pipeline
3 $2.29 1.627 Launch
rtxa5000-4.16.128.160.nvlink
32,768.0
tensor
4 $2.34 3.122 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 3.123 Launch
rtx4090-3.16.96.160
32,768.0
pipeline
3 $2.83 1.616 Launch
rtx3090-4.16.64.160
32,768.0
tensor
4 $2.89 3.512 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 1.271 Launch
rtx4090-4.16.64.160
32,768.0
tensor
4 $3.60 3.497 Launch
h100-1.16.64.160
32,768.0
1 $3.83 3.117 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 4.396 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 10.084 Launch
h200-1.16.128.160
32,768.0
1 $4.74 8.694 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 21.225 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
h100nvl-1.16.96.240
32,768.0
1 $4.12 1.051 Launch
h200-1.16.128.240
32,768.0
1 $4.74 5.348 Launch
teslaa100-2.24.256.240
32,768.0
tensor
2 $4.93 6.739 Launch
teslaa100-2.24.256.320.nvlink
32,768.0
tensor
2 $4.94 6.739 Launch
rtx5090-4.16.128.320
32,768.0
tensor
4 $5.76 3.034 Launch
rtx4090-6.44.256.240
32,768.0
pipeline
6 $5.84 3.461 Launch
dedicated-rtx3090-8.64.128.960-1
32,768.0
tensor
8 $6.04 7.517 Launch
rtx4090-8.44.256.240
32,768.0
tensor
8 $7.52 7.488 Launch
h100-2.24.256.240
32,768.0
tensor
2 $7.85 36.330 6.726 Launch
h200-2.24.256.240.nvlink
32,768.0
tensor
2 $9.41 17.880 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa100-3.32.384.240
32,768.0
pipeline
3 $7.36 7.171 Launch
rtx4090-8.44.256.240
32,768.0
tensor
8 $7.52 1.128 Launch
h100nvl-2.24.192.240
32,768.0
tensor
2 $8.17 2.926 Launch
rtx5090-6.44.256.240
32,768.0
pipeline
6 $8.86 1.105 Launch
teslaa100-4.16.256.240
32,768.0
tensor
4 $9.14 14.301 Launch
h200-2.24.256.240
32,768.0
tensor
2 $9.41 11.521 Launch
h200-2.24.256.240.nvlink
32,768.0
tensor
2 $9.41 11.521 Launch
teslaa100-4.32.384.320.nvlink
32,768.0
tensor
4 $9.50 14.301 Launch
rtx5090-8.44.256.240
32,768.0
tensor
8 $11.55 6.891 Launch
h100-3.32.384.240
32,768.0
pipeline
3 $11.73 7.152 Launch
h100-4.16.256.240
32,768.0
tensor
4 $14.96 14.276 Launch

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