You can run a DeepSeek model locally with Ollama: install Ollama, choose a smaller DeepSeek-R1 distilled model, and run its model tag in a terminal. For example, ollama run deepseek-r1:7b downloads and starts the 7B model. The listed download is 4.7 GB, but that figure alone does not tell you how much RAM or VRAM the model will need while running.
How do I run DeepSeek locally?
For a first experiment, use Ollama with a distilled DeepSeek-R1 model rather than attempting the full 671B-parameter checkpoint. DeepSeek’s official R1 repository lists distilled models from 1.5B to 70B parameters, alongside the full-size model. Ollama documents a straightforward terminal command for downloading and running them.
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Install Ollama using its official download page. Check the current instructions for your operating system and confirm the runtime supports your system.
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Choose a model tag that fits your available storage and is plausible for your hardware. Smaller options are a better starting point on less capable computers, but file size is not a guarantee that the model will load or run at a useful speed.
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Open a terminal and enter the command for the model you chose. For example:
ollama run deepseek-r1:7bOllama also documents
ollama run deepseek-r1:8b. The default command,ollama run deepseek-r1, is available too, but choose a size-specific tag when you want to control which variant you download. -
Wait for the model files to download. When Ollama starts the model, enter a short prompt and press Enter. A successful response confirms that the model loaded; it does not establish that longer prompts or sustained use will perform well on your computer.
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For additional tags and current listed sizes, see the Ollama DeepSeek-R1 model page. Runtime behavior depends on the model variant, quantization, context length, runtime settings, and the computer’s available memory and compute.
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DeepSeek-R1’s official repository lists distilled 1.5B, 7B, 8B, 14B, 32B, and 70B models, as well as the full 671B R1 and R1-Zero models. The distilled models are much smaller and are the practical place to begin for a local experiment. The parameter count describes model scale; it does not, by itself, specify a system’s required memory.
| Ollama DeepSeek-R1 tag/model size | Listed download size |
|---|---|
| 1.5B | 1.1 GB |
| 7B | 4.7 GB |
| 8B | 5.2 GB |
| 14B | 9.0 GB |
| 32B | 20 GB |
| 70B | 43 GB |
| 671B | 404 GB |
These are the file sizes listed by Ollama’s model library, accessed in 2026; they are not universal RAM or VRAM requirements. Choose the smallest model that suits your experiment, then move up only if your machine can load it and its speed and quality meet your needs. A large model file can also take substantial disk space; an external SSD may help with storage, but it does not supply the memory or compute needed for inference.
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How much space and memory does DeepSeek need?
Plan disk space using the download size for the specific tag, and leave additional room for the operating system, other files, and runtime needs. Do not treat the download figure as the amount of RAM or VRAM the model requires. During inference, memory use can also depend on precision or quantization, context length, batch size, runtime overhead, and whether model weights are divided across devices.
There is no single minimum hardware specification established for all DeepSeek models, quantizations, contexts, and runtimes. As an example of why one number would be misleading, DeepSeek’s older DeepSeek-LLM documentation reports peak memory of 13.29 GB to 21.25 GB for a particular 7B profile on one A100 40 GB GPU, with batch size 1 and sequence lengths from 256 to 4096. Its cited 67B profile uses eight A100-PCIE-40GB GPUs. These are measurements for those documented configurations, not current consumer-PC recommendations.
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Can I run DeepSeek on my PC without a GPU?
The cited model-library and repository pages do not establish a universal CPU-only hardware requirement or promise usable performance without a GPU. Whether a model can load and respond acceptably depends on the chosen model, runtime, quantization, context, and available system resources. Start with a small distilled model and test it on your own machine; if it fails to load or responds too slowly, try a smaller variant or a supported quantized/runtime configuration. The sources do not justify a specific quantization recommendation or a guarantee for a particular PC.
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What does running the full DeepSeek model locally involve?
The full DeepSeek-R1 checkpoint is 671B parameters. DeepSeek-V3 is also a 671B-parameter mixture-of-experts model, with 37B parameters activated, according to DeepSeek’s repository. These full-scale models are a different undertaking from running a small distilled model through Ollama. DeepSeek’s V3 documentation describes distributed inference across machines, rather than a simple beginner setup.
For advanced deployment, DeepSeek lists DeepSeek-Infer, SGLang, LMDeploy, TensorRT-LLM, vLLM, and LightLLM, as well as support paths for AMD GPUs through SGLang and Huawei Ascend. Compatibility, supported precision, and launch requirements can change, so consult the current documentation for the framework and hardware you intend to use. The V3 repository describes tensor and pipeline parallelism across network-connected machines.
DeepSeek’s own V3 demo has narrower prerequisites: its repository describes a Linux and Python 3.10 setup, model download and conversion steps, and a torchrun example with two nodes and eight processes per node. That is an example for the documented demo, not a requirement for every third-party runtime. In the demo section, DeepSeek says, “Hugging Face’s Transformers has not been directly supported yet.” That statement applies to the repository’s V3 demo context, not every community implementation.
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What to try if a local model will not run well
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It will not load: Check that you selected the intended tag and have enough available memory for that model and runtime. Try a smaller distilled variant.
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It runs too slowly: Reduce the model size or use a quantized/runtime configuration supported by your chosen software. Speed depends on the computer and settings; the listed file size does not predict response speed.
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The download will not fit: Choose a smaller tag or make room on the drive. An external SSD can add storage capacity, but it will not replace adequate inference memory or compute.
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You need a large checkpoint: Follow the current instructions for a suitable inference framework and verify its hardware and deployment requirements. DeepSeek’s V3 demo is a multi-node Linux/Python example, not an Ollama-style one-command setup.
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