GLM-Z1-9B-0414

reasoning

GLM-Z1-9B-0414 is a 9.4-billion-parameter model from the new GLM-4-0414 series, which came as a genuine surprise even to its developers. Despite its relatively small parameter count, it was trained using all the key techniques employed in creating much larger models within this series. This approach has resulted in unexpectedly high levels of accuracy, logical reasoning, and overall performance.

The model underwent comprehensive training, starting with pre-training on vast volumes of high-quality data, and continuing through advanced post-training stages (preference alignment, rejection sampling, and reinforcement learning based on pairwise ranking feedback). These methods enabled the model to better understand what constitutes the most helpful and accurate responses in specific situations.

Special emphasis was placed on developing reasoning capabilities during training, particularly for solving mathematical problems and logical puzzles. These skills make the model effective not only in standard question-answer scenarios but also in handling more complex analytical tasks. Furthermore, thanks to its compact size, GLM-Z1-9B-0414 demonstrates excellent computational efficiency, making it an ideal choice for resource-constrained environments.


Announce Date: 14.04.2025
Parameters: 10B
Context: 33K
Layers: 40
Attention Type: Full Attention
Developer: Z.ai
Transformers Version: 4.52.0.dev0
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore GLM-Z1-9B-0414 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 GLM-Z1-9B-0414

Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-1.16.16.160
32,768.0
1 $0.33 3.089 Launch
teslaa2-1.16.32.160
32,768.0
1 $0.38 3.118 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 8.933 Launch
rtx2080ti-2.12.64.160
32,768.0
tensor
2 $0.69 5.277 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 9.713 Launch
rtx3080-2.16.32.160
32,768.0
tensor
2 $0.97 3.879 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 9.684 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 22.853 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 15.446 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 50.701 Launch
h100-1.16.64.160
32,768.0
1 $3.83 107.920 50.649 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 60.887 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 106.389 Launch
h200-1.16.128.160
32,768.0
1 $4.74 95.266 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 195.518 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-1.16.16.160
32,768.0
1 $0.33 1.073 Launch
teslaa2-1.16.32.160
32,768.0
1 $0.38 1.102 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 6.916 Launch
rtx2080ti-2.12.64.160
32,768.0
tensor
2 $0.69 3.261 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 7.697 Launch
rtx3080-2.16.32.160
32,768.0
tensor
2 $0.97 1.862 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 7.667 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 20.836 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 13.430 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 48.684 Launch
h100-1.16.64.160
32,768.0
1 $3.83 48.633 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 58.871 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 104.372 Launch
h200-1.16.128.160
32,768.0
1 $4.74 93.249 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 193.502 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-2.16.32.160
32,768.0
tensor
2 $0.54 2.145 Launch
teslaa2-2.16.32.160
32,768.0
tensor
2 $0.57 2.204 Launch
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 13.832 Launch
rtx2080ti-4.16.32.160
32,768.0
tensor
4 $1.12 3.261 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 13.832 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 15.393 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 6.426 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 1.862 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 15.334 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 41.680 Launch
h100-1.16.64.160
32,768.0
1 $3.83 41.629 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 51.867 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 97.368 Launch
h200-1.16.128.160
32,768.0
1 $4.74 86.245 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 186.498 Launch

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