The web interface, as well as the official API, use the actual, full, 671B R1 model. ALL other "R1" models are finetunes of existing architectures (Llama 3.3 and Qwen 2.5). Answer from Zalathustra on reddit.com
Hugging Face
huggingface.co › deepseek-ai › DeepSeek-R1
deepseek-ai/DeepSeek-R1 · Hugging Face
1 month ago - DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.
Reddit
reddit.com › r/localllama › what model is deepseek-r1 online?
r/LocalLLaMA on Reddit: What model is DeepSeek-R1 online?
January 28, 2025 -
excuseme if this is dumb question, im complete amateur in this, but im curious: i know you can download different models of DeepSeek-R1 localy and they ranging from 1,5gb size up to 402gb.. but what of this models use online version of DeepSeek? Thank you.
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The web interface, as well as the official API, use the actual, full, 671B R1 model. ALL other "R1" models are finetunes of existing architectures (Llama 3.3 and Qwen 2.5).
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Unless you have a server farm of R100's you are downloading Llama and Qwen models fine tuned on DeepSeek R1 reasoning traces. These fine tunes/distills make the models 'smarter' - better able to solve reasoning tasks, but they are smarter relative to their base model. If you start with a 'dumb model' making it smarter doesn't make it a genius, just 'less dumb'. According to reports the 32B Qwen distill is about the equivalent of o1-mini. To run the 'real DeepSeek R1' requires absurd hardware or tricks to run it efficiently.
Yes, you can run DeepSeek-R1 locally on your device (20GB RAM min.)
Props for your work! sum of your VRAM+CPU = 80GB+ This should read "VRAM+RAM", shouldn't it? More on reddit.com
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February 13, 2025Hardware requirements for running the full size deepseek R1 with ollama?
Step 1: Acquire ~25 4090's Step 2: Network them together Step 3: Start homebrew nuclear cold-fusion reactor Step 4: Plug into cluster More on reddit.com
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Snowkylin
snowkylin.github.io › blogs › a-note-on-deepseek-r1.html
A Note on DeepSeek R1 Deployment
January 30, 2025 - The original DeepSeek R1 671B model is 720GB in size, which is huge. Even a $200k monster like NVIDIA DGX H100 (with 8xH100) can barely hold it. Here I use Unsloth AI’s dynamically quantized version, which selectively quantize a few important layers to higher bits, while leaving most of the ...
Fireworks AI
fireworks.ai › blog › deepseek-r1-deepdive
DeepSeek-R1 Overview: Features, Capabilities, Parameters
Operational expenses are estimated ... spend on OpenAI’s o1 model**.** Cost of running DeepSeek R1 on Fireworks AI is $8/ 1 M token (both input & output), whereas, running OpenAI o1 model costs $15/ 1M input tokens and $60/ ...
GitHub
github.com › deepseek-ai › DeepSeek-R1
GitHub - deepseek-ai/DeepSeek-R1
DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen.
Starred by 91.6K users
Forked by 11.8K users
DeepSeek
api-docs.deepseek.com › models & pricing
Models & Pricing | DeepSeek API Docs
The prices listed below are in units of per 1M tokens. A token, the smallest unit of text that the model recognizes, can be a word, a number, or even a punctuation mark. We will bill based on the total number of input and output tokens by the model.
Towards AI
pub.towardsai.net › deepseek-r1-model-architecture-853fefac7050
DeepSeek-R1: Model Architecture. This article provides an in-depth… | by Shakti Wadekar | Towards AI
March 13, 2025 - Efficient Learning — Experts specialize in different aspects of data, improving generalization. Computation Savings — Since only a subset of experts is used per token, MoE models are cheaper to run compared to dense models of the same size. DeepSeek-V3/R1 has 671 billion total parameters, of ...
AWS
aws.amazon.com › blogs › machine-learning › deepseek-r1-model-now-available-in-amazon-bedrock-marketplace-and-amazon-sagemaker-jumpstart
DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart | Artificial Intelligence
February 5, 2025 - DeepSeek-R1 uses a Mixture of Experts (MoE) architecture and is 671 billion parameters in size.
OpenRouter
openrouter.ai › deepseek › deepseek-r1
R1 - API, Providers, Stats | OpenRouter
DeepSeek R1 is here: Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass.
DEV Community
dev.to › askyt › deepseek-r1-671b-complete-hardware-requirements-optimal-deployment-setup-2e48
DeepSeek-R1 671B: Complete Hardware Requirements - DEV Community
February 6, 2025 - With 671 billion parameters, it matches the performance of leading models like OpenAI’s GPT-4, excelling in tasks such as mathematics, coding, and complex reasoning. The model was trained using 2,048 NVIDIA H800 GPUs over approximately two ...
Ollama
ollama.com › library › deepseek-r1
deepseek-r1
DeepSeek-R1 has received a minor version upgrade to DeepSeek-R1-0528 for the 8 billion parameter distilled model and the full 671 billion parameter model. In this update, DeepSeek R1 has significantly improved its reasoning and inference ...
BentoML
bentoml.com › blog › the-complete-guide-to-deepseek-models-from-v3-to-r1-and-beyond
The Complete Guide to DeepSeek Models: V3, R1, V3.1, V3.2 and Beyond
Instead of training new models from scratch, DeepSeek took a smart shortcut: Started with 6 open-source models from Llama 3.1/3.3 and Qwen 2.5 · Generated 800,000 high-quality reasoning samples using R1 · Fine-tuned the smaller models on these synthetic reasoning data · Unlike R1, these distilled models rely solely on SFT and they do not include an RL stage. Despite their smaller size, these models perform remarkably well on reasoning tasks, proving that large-scale AI reasoning can be efficiently distilled.
Reddit
reddit.com › r/selfhosted › yes, you can run deepseek-r1 locally on your device (20gb ram min.)
r/selfhosted on Reddit: Yes, you can run DeepSeek-R1 locally on your device (20GB RAM min.)
February 13, 2025 -
I've recently seen some misconceptions that you can't run DeepSeek-R1 locally on your own device. Last weekend, we were busy trying to make you guys have the ability to run the actual R1 (non-distilled) model with just an RTX 4090 (24GB VRAM) which gives at least 2-3 tokens/second.
Over the weekend, we at Unsloth (currently a team of just 2 brothers) studied R1's architecture, then selectively quantized layers to 1.58-bit, 2-bit etc. which vastly outperforms basic versions with minimal compute.
We shrank R1, the 671B parameter model from 720GB to just 131GB (a 80% size reduction) whilst making it still fully functional and great
No the dynamic GGUFs does not work directly with Ollama but it does work on llama.cpp as they support sharded GGUFs and disk mmap offloading. For Ollama, you will need to merge the GGUFs manually using llama.cpp.
Minimum requirements: a CPU with 20GB of RAM (but it will be very slow) - and 140GB of diskspace (to download the model weights)
Optimal requirements: sum of your VRAM+RAM= 80GB+ (this will be somewhat ok)
No, you do not need hundreds of RAM+VRAM but if you have it, you can get 140 tokens per second for throughput & 14 tokens/s for single user inference with 2xH100
Our open-source GitHub repo: github.com/unslothai/unsloth
Many people have tried running the dynamic GGUFs on their potato devices and it works very well (including mine).
R1 GGUFs uploaded to Hugging Face: huggingface.co/unsloth/DeepSeek-R1-GGUF
To run your own R1 locally we have instructions + details: unsloth.ai/blog/deepseekr1-dynamic
LLM Stats
llm-stats.com › home › models › deepseek-r1
DeepSeek-R1: Pricing, Context Window, Benchmarks, and More
January 20, 2025 - DeepSeek-R1 has 671.0 billion parameters.
Reddit
reddit.com › r/ollama › hardware requirements for running the full size deepseek r1 with ollama?
r/ollama on Reddit: Hardware requirements for running the full size deepseek R1 with ollama?
October 1, 2024 -
My machine runs the Deepseek R1-14B model fine, but the 34B and 70B are too slow for practical use. I am looking at building a machine capable of running the full 671B model fast enough that it's not too annoying as a coding assistant. What kind of hardware do i need?
Tenable®
tenable.com › blog › frequently-asked-questions-about-deepseek-large-language-model-llm-v3-r1
Frequently Asked Questions About DeepSeek Large Language Model (LLM) - Blog | Tenable®
August 5, 2025 - DeepSeek R1 has 671 billion parameters and requires multiple expensive high-end GPUs to run. There are distilled versions of the model starting at 1.5 billion parameters, going all the way up to 70 billion parameters.
LM Studio
lmstudio.ai › blog › deepseek-r1
DeepSeek R1: open source reasoning model | LM Studio Blog
January 29, 2025 - According to several popular reasoning benchmarks like AIME 2024, MATH-500, and CodeForces, the open-source flagship 671B parameter DeepSeek-R1 model performs comparably to OpenAI's full-sized o1 reasoning model.
Built In
builtin.com › artificial-intelligence › deepseek-r1
What Is DeepSeek-R1? | Built In
DeepSeek-R1 has 671 billion parameters in total. But DeepSeek also released six “distilled” versions of R1, ranging in size from 1.5 billion parameters to 70 billion parameters.
Published October 6, 2025
GitHub
github.blog › home › changelogs › deepseek-r1 is now available in github models (public preview)
DeepSeek-R1 is now available in GitHub Models (Public Preview) - GitHub Changelog
April 30, 2025 - DeepSeek-R1 is a 671B parameter AI model designed to enhance deep learning, natural language processing, and computer vision capabilities.
Weights & Biases
wandb.ai › byyoung3 › ml-news › reports › Unsloth-unveils-1-58-bit-DeepSeek-R1--VmlldzoxMTEyMjU3Mg
Unsloth unveils 1.58 bit DeepSeek R1 | ml-news
1 week ago - Weights & Biases, developer tools for machine learning