DeepSeek-R1-0528-Qwen3-8B

DeepSeek-R1-0528-Qwen3-8B is a compact 8-billion-parameter model created by distilling knowledge and reasoning capabilities from the flagship DeepSeek-R1-0528 into the Qwen3-8B base model. The model uses an architecture identical to Qwen3-8B, but incorporates the tokenizer from DeepSeek-R1-0528, ensuring compatibility with more advanced reasoning capabilities.

It demonstrates outstanding performance, achieving 86.0% on AIME 2024 — exceeding the base Qwen3-8B by 10% and matching the performance of the much larger Qwen3-235B-Thinking. These results, along with strong benchmark scores across other evaluations, place it among the leading open-source models in its class. The model serves as a great example of a well-implemented distillation process. Reasoning chains from DeepSeek-R1-0528 have been successfully transferred into a more compact architecture, opening new possibilities for academic research and industrial development of small, specialized models. Its compact size of 8B parameters makes it accessible for deployment on less powerful hardware while maintaining high-quality reasoning abilities.

DeepSeek-R1-0528-Qwen3-8B is ideally suited for educational applications, small-scale research projects, and any scenario where a capable reasoning-style answering model is needed, but deploying large reasoning models is not feasible.


Announce Date: 28.05.2025
Parameters: 8.19B
Context: 131K
Attention Type: Full or Sliding Window Attention
VRAM requirements: 21.8 GB using 4 bits quantization
Developer: DeepSeek
Transformers Version: 4.51.0
License: Apache 2.0

Public endpoint

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Private server

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We recommend deploying private instances in the following scenarios:

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Recommended configurations for hosting DeepSeek-R1-0528-Qwen3-8B

Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
teslat4-2.16.32.160 16 32768 160 2 $0.80 Launch
teslaa10-2.16.64.160 16 65536 160 2 $0.93 Launch
rtx2080ti-3.16.64.160 16 65536 160 3 $0.95 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx3080-3.16.64.160 16 65536 160 3 $1.43 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
rtx3090-2.16.64.160 16 65536 160 2 $1.67 Launch
rtx4090-2.16.64.160 16 65536 160 2 $2.19 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
teslat4-2.16.32.160 16 32768 160 2 $0.80 Launch
teslaa10-2.16.64.160 16 65536 160 2 $0.93 Launch
rtx2080ti-3.16.64.160 16 65536 160 3 $0.95 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx3080-3.16.64.160 16 65536 160 3 $1.43 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
rtx3090-2.16.64.160 16 65536 160 2 $1.67 Launch
rtx4090-2.16.64.160 16 65536 160 2 $2.19 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa10-2.16.64.160 16 65536 160 2 $0.93 Launch
rtx2080ti-4.16.64.160 16 65536 160 4 $1.18 Launch
teslat4-4.16.64.160 16 65536 160 4 $1.48 Launch
rtx3090-2.16.64.160 16 65536 160 2 $1.67 Launch
rtx3080-4.16.64.160 16 65536 160 4 $1.82 Launch
rtx4090-2.16.64.160 16 65536 160 2 $2.19 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
rtx5090-2.16.64.160 16 65536 160 2 $2.93 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch

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