✅ Model Download Instructions for DeepSeek and Other Open Source LLMs (2025 Guide)

ds66

2024-12-26

🔍 Introduction

Large Language Models (LLMs) like DeepSeek, Mistral, LLaMA, and others have taken center stage in the AI revolution. But many developers and organizations are moving away from closed-source APIs (like OpenAI) in favor of self-hosted, open-source, or offline models for reasons of privacy, control, and cost.

This in-depth guide will walk you through:

Downloading DeepSeek models using Ollama, Hugging Face, or direct methods
Supported formats and compatible backends (llama.cpp, vLLM, LMDeploy)
Efficient storage tips for large models
Verifying model integrity and compatibility
Fine-tuning, quantization, and running locally
Common issues and troubleshooting

By the end, you'll be ready to run massive models like DeepSeek R1-Chat, even on consumer-grade hardware.

✅ Table of Contents

Why Download Models Locally?
DeepSeek Model Overview
Option 1: Download via Ollama
Option 2: Download via Hugging Face CLI
Option 3: Direct Download (Torrent/Git LFS)
Quantization Formats (GGUF, FP16, Q4_K_M, etc.)
Compatible Runtimes: llama.cpp, LMDeploy, vLLM
How Much Storage and RAM Do You Need?
GPU Acceleration (NVIDIA, AMD, M-Series Apple Silicon)
Tips for Multiple Models
Verifying and Updating Models
Conclusion + Recommended Resources

1. 💡 Why Download LLMs Locally?

Reason	Benefit
Privacy	All prompts and replies stay on your device
Cost Control	No usage fees, API key issues, or limits
Customization	Use your own embeddings, fine-tuning, memory
Offline Access	No internet required to chat or generate
Speed	No network latency, full GPU/CPU control

2. 🧠 DeepSeek Model Overview

Variant	Parameters	Use Case
DeepSeek-Coder	6.7B, 33B	Code generation and completion
DeepSeek-MoE	236B (37B active)	General LLM reasoning, fast inference
DeepSeek-Chat	67B+	Conversational chatbot
DeepSeek-R1	671B (Mixture of Experts)	Research-scale AGI model (partial access)

Most DeepSeek models are open-access and are downloadable for research and commercial use under specific licenses.

3. 🐳 Option 1: Download with Ollama (Recommended)

Step 1: Install Ollama

bash
curl -fsSL https://ollama.com/install.sh | sh

Step 2: Pull DeepSeek Model

bash
ollama pull deepseek-chat

This automatically downloads a quantized GGUF model optimized for llama.cpp.

You can also list all available models:

bash
ollama list

Run the model:

bash
ollama run deepseek-chat

Ollama stores models in:
Linux/macOS: ~/.ollama
Windows: C:\Users\<name>\.ollama

4. 💾 Option 2: Download via Hugging Face CLI

Step 1: Install `huggingface_hub`

bash
pip install huggingface_hub

Step 2: Login (optional for some models)

bash
huggingface-cli login

Step 3: Download DeepSeek Models

Example (DeepSeek-Coder 6.7B):

bash
huggingface-cli repo download deepseek-ai/deepseek-coder-6.7b-base

Or clone with Git LFS:

bash
git lfs install
git clone https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base

5. 🌐 Option 3: Direct Download (Torrent / Git)

Some models are hosted as .gguf, .bin, or .safetensors on:

Torrent links
Academic FTPs
Chinese AI sharing platforms (e.g. ModelScope)
Community repos like TheBloke

Example:

bash
wget https://huggingface.co/TheBloke/deepseek-chat-7B-GGUF/resolve/main/deepseek-chat.Q4_K_M.gguf

6. 📦 Quantization Formats Explained

Format	Use Case	RAM Needed	Speed
FP16	Full-precision, for fine-tuning	High	Slower
INT4 / Q4_K_M	Optimized for llama.cpp	Low	Fast
GGUF	New standard for llama.cpp models	Medium	Very Fast
Safetensors	Used in PyTorch/HF ecosystem	High	Flexible

Use tools like llama.cpp, lmdeploy, or ctransformers to convert between formats.

7. 🧩 Compatible Inference Runtimes

✅ llama.cpp (CPU / Metal / CUDA)

Best for quantized .gguf
CLI, server, API endpoints
Supports 4-bit, streaming, long context

bash
./main -m deepseek-chat.Q4_K_M.gguf

✅ LMDeploy (NVIDIA only)

bash
git clone https://github.com/InternLM/lmdeploy

Supports tensor parallel, multi-GPU, Triton backend.

✅ vLLM

Fastest for multi-user scenarios
Python-first, supports Hugging Face models
Compatible with FP16, quantized

8. 🗃️ How Much Storage and RAM?

Model	Disk Size	RAM (min)
DeepSeek 7B Q4_K_M	~4.1 GB	6–8 GB
DeepSeek 33B GGUF	~20–30 GB	24–32 GB
DeepSeek R1 236B	~100+ GB	128 GB+ (or tensor parallel)
Full R1 (671B)	Research only	GPU cluster required

You can run Q4_K_M on MacBook Air M1, while full FP16 models require RTX 3090 or A100 GPUs.

9. ⚡ GPU Acceleration

Platform	Tool	Notes
NVIDIA	LMDeploy, vLLM	Fastest, FP16 or INT8
AMD	ROCm + llama.cpp	Experimental support
Apple M1/M2	llama.cpp + Metal	Very efficient
CPU	llama.cpp	Good for small quantized models

For DeepSeek on GPU:

bash
# Using vLLMpip install vllm
python3 -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/deepseek-coder-33b-base

10. 📚 Tips for Managing Multiple Models

Use symbolic links to keep one copy in shared directory
Add model_config.yaml to each model’s folder
Use Docker volumes to mount external model storage
Use scripts to switch between models

bash
alias chat-coder="ollama run deepseek-coder"alias chat-chat="ollama run "

11. ✅ Verifying and Updating Models

Check model checksum

bash
sha256sum deepseek-chat.Q4_K_M.gguf

Compare with publisher's value.

Update models in Ollama

bash
ollama update
ollama pull deepseek-chat

Use Docker tags to avoid breaking changes:

yaml
image: ollama/ollama:0.1.23

12. 🧳 Conclusion + Next Steps

You've now learned how to download, manage, and run DeepSeek and other open models locally. Whether you're building a chatbot, a dev assistant, or an AI-powered app, you now control your own stack—no third-party API required.