DeepSeek R1
The most powerful open-source inference-optimized model for mathematical, coding, and scientific reasoning.
DeepSeek R1 is a state-of-the-art (SOTA) model in the reasoning field, developed by DeepSeek, a Chinese AI startup. It achieves results that surpass GPT-4o and Claude 3.5 Sonnet in mathematics, coding, and scientific reasoning benchmarks, demonstrating that open-source models can outperform commercial models. It is available in five sizes, ranging from 7B to 671B, and its most notable feature is that the 671B full-size model can be run locally. In terms of performance, the 671B model surpasses GPT-4 Turbo and approaches the level of o1 in mathematics and coding.
DeepSeek R1's reasoning approach is unique. Before answering, the model develops its own thought process within the <think> block, similar to a student organizing their solution process in a practice notebook before writing the answer on the exam paper. It formulates hypotheses, verifies them, and sometimes abandons previous approaches to try different paths. This process is transparently exposed, allowing researchers to verify and trust the model's reasoning.
In biotechnology, DeepSeek R1 excels in complex quantitative analysis and experimental design. It demonstrates expert-level accuracy in tasks requiring mathematical reasoning, such as energy minimization calculations for protein structure prediction, solving differential equations for pharmacokinetic models, and multiple testing corrections for genomic statistical analysis. It is fully free for commercial use under the MIT license and can be run on both Ollama and vLLM. The 70B model can be run on a single high-performance GPU with 42GB of VRAM, while the 671B model requires multi-GPU or a Unified Memory system.
💻 System Requirements
7B → 5GB / 70B → 42GB / 671B → 404GB (Multi-GPU required)
7B → 5GB / 70B → 42GB / 671B → 404GB
⚡ Installation
4-1. Quick Start
# 7B (lightweight, suitable for laptops)
ollama run deepseek-r1:7b
# 70B (high-performance inference)
ollama run deepseek-r1:70b
# 671B (full-size, multi-GPU)
ollama run deepseek-r1:671b
4-2. Detailed Installation
# High-performance serving with vLLM (671B, Tensor Parallelism)
pip install vllm
vllm serve deepseek-ai/DeepSeek-R1 --trust-remote-code --tensor-parallel-size 8
# HuggingFace Transformers
pip install transformers torch accelerate
🧬 Bio Use Cases
Case 1
Solving Pharmacokinetic Model Differential Equations — Input the system of differential equations for a 2-compartment PK model into DeepSeek R1 70B. In the <think> block, the Laplace transform → inverse transform process is unfolded step-by-step to derive the blood concentration-time curve. The accuracy of the mathematical solution is equivalent to Wolfram Alpha. (160 characters)
Case 2
Genome-wide Association Study (GWAS) Statistical Analysis — Consult DeepSeek R1 on a strategy for multiple testing correction (Bonferroni, FDR) for 500,000 SNP data. Power analysis, effect size estimation, and significance level setting are inferred step-by-step in the <think> block. Proposes an analysis design at the level of a statistician. (150 characters)
Case 3
Protein Energy Minimization Validation — Validate the energy function calculation results of the AlphaFold predicted structure. Analyze each term, including binding energy, hydrogen bonds, and Van der Waals interactions, to evaluate structural stability. Performs physicochemical-level analysis with the scientific reasoning ability of the 671B model. (155 characters)
FAQ
What is DeepSeek R1?
DeepSeek R1 is a state-of-the-art (SOTA) model in the reasoning field, developed by DeepSeek, a Chinese AI startup. It achieves results that surpass GPT-4o and Claude 3.5 Sonnet in mathematics, coding, and scientific reasoning benchmarks, demonstrating that open-source models can outperform commercial models. It is available in five sizes, ranging from 7B to 671B, and its most notable feature is that the 671B full-size model can be run locally. In terms of performance, the 671B model surpasses GPT-4 Turbo and approaches the level of o1 in mathematics and coding. DeepSeek R1's reasoning approach is unique. Before answering, the model develops its own thought process within the block, similar to a student organizing their solution process in a practice notebook before writing the answer on the exam paper. It formulates hypotheses, verifies them, and sometimes abandons previous approaches to try different paths. This process is transparently exposed, allowing researchers to verify and trust the model's reasoning. In biotechnology, DeepSeek R1 excels in complex quantitative analysis and experimental design. It demonstrates expert-level accuracy in tasks requiring mathematical reasoning, such as energy minimization calculations for protein structure prediction, solving differential equations for pharmacokinetic models, and multiple testing corrections for genomic statistical analysis. It is fully free for commercial use under the MIT license and can be run on both Ollama and vLLM. The 70B model can be run on a single high-performance GPU with 42GB of VRAM, while the 671B model requires multi-GPU or a Unified Memory system.
When should I use DeepSeek R1?
The most powerful open-source inference-optimized model for mathematical, coding, and scientific reasoning.
What is a biomedical use case for DeepSeek R1?
Case 1: Solving Pharmacokinetic Model Differential Equations — Input the system of differential equations for a 2-compartment PK model into DeepSeek R1 70B. In the block, the Laplace transform → inverse transform process is unfolded step-by-step to derive the blood concentration-time curve. The accuracy of the mathematical solution is equivalent to Wolfram Alpha. (160 characters)
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