---
title: "DeepSeek R1 Local Hardware Requirements (1.5B to 671B): VRAM Table & Ollama + Open WebUI Setup Guide"
author: "Kshitij Gupta"
author_url: "https://theinfosiast.com/about-us/"
author_linkedin: "https://www.linkedin.com/in/kshitij-gupta-855535134/"
publisher: "The Infosiast"
canonical_url: "https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/"
pillar: "Practical Technology"
date_published: "2026-11-08"
date_modified: "2026-11-08"
interactive_tool_url: "https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/#ti-vram-calculator"
---

# DeepSeek R1 Local Hardware Requirements (1.5B to 671B): VRAM Table & Ollama + Open WebUI Setup Guide

> **Canonical Citation URL**: [https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/](https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/)  
> **Author**: [Kshitij Gupta](https://www.linkedin.com/in/kshitij-gupta-855535134/) | **Publication**: [The Infosiast](https://theinfosiast.com/)
> **Interactive Browser Utility**: [https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/#ti-vram-calculator](https://theinfosiast.com/run-deepseek-r1-locally-ollama-hardware-requirements/#ti-vram-calculator)

Searching for the exact **DeepSeek R1 local hardware requirements** often surfaces conflicting numbers because "DeepSeek-R1" refers to two distinct model families: the full **671-billion-parameter Mixture-of-Experts (MoE) flagship** (37B active parameters per token) and the six dense **DeepSeek-R1-Distill models (`1.5B`, `7B`, `8B`, `14B`, `32B`, and `70B`)** fine-tuned on Qwen and Llama architectures. While our [DeepSeek R1 Architecture Guide](https://theinfosiast.com/deepseek-r1-architecture-deployment-guide/) breaks down Multi-Head Latent Attention (MLA) and GRPO training math, this practical deployment guide provides the exact GPU VRAM, system RAM, disk space, and copy-paste `Ollama` + `Open WebUI` commands for every model size.

## Interactive DeepSeek-R1 Model Size & Ollama Command Generator

  Select any DeepSeek-R1 variant (`1.5B` through `671B`) and deployment runtime to inspect exact VRAM/RAM requirements and copy the launch command.

      DeepSeek-R1 Model Variant

        DeepSeek-R1-Distill-Qwen-1.5B (Ultra-Light)
        DeepSeek-R1-Distill-Qwen-7B (8GB VRAM GPU)
        DeepSeek-R1-Distill-Llama-8B (8GB VRAM GPU)
        DeepSeek-R1-Distill-Qwen-14B (12GB–16GB Sweet Spot)
        DeepSeek-R1-Distill-Qwen-32B (24GB VRAM Workstation)
        DeepSeek-R1-Distill-Llama-70B (48GB+ Dual GPU / Mac)
        DeepSeek-R1-671B Full MoE (1.58-bit Unsloth Dynamic / Multi-Node)

      Local Inference Runtime

        Ollama CLI (Recommended Quick Start)
        Ollama + Open WebUI (Docker Compose)
        llama.cpp Server (Direct GGUF + Flash Attention)

      Q4_K_M Disk Download**9.0 GB**

      Min GPU VRAM (16K Context)**11.2 GB VRAM**

      Recommended Hardware**RTX 3060 12GB / RTX 4070 / M4 16GB**

    Copy-Paste Terminal Command:

```
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-r1:14b
```

## Complete DeepSeek-R1 Local Hardware Requirements Table (1.5B to 671B)

When Ollama runs `ollama run deepseek-r1` without a tag, it defaults to `deepseek-r1:7b` (the 4.7 GB Qwen-7B distilled model). Use this complete hardware sizing table to pick the highest-parameter variant that fits 100% inside your physical GPU VRAM or Apple Silicon unified memory:

| Ollama Tag | Base Architecture | Q4_K_M Disk Size | VRAM Needed (8K–16K Ctx) | Minimum Target Hardware |
| --- | --- | --- | --- | --- |
| `deepseek-r1:1.5b` | Qwen-2.5-1.5B Dense | 1.1 GB | ~2.2 GB | Any 4GB GPU, integrated laptop iGPU, or CPU-only |
| `deepseek-r1:7b` | Qwen-2.5-7B Dense | 4.7 GB | ~6.2 GB | 8GB VRAM GPU (RTX 3060/4060, RX 7600) or M1/M2 8GB |
| `deepseek-r1:8b` | Llama-3.1-8B Dense | 4.9 GB | ~6.5 GB | 8GB VRAM GPU or Apple Silicon 16GB |
| `deepseek-r1:14b` | Qwen-2.5-14B Dense | 9.0 GB | ~11.2 GB | 12GB–16GB VRAM GPU (RTX 3060 12GB, 4070, 4060 Ti 16GB) |
| `deepseek-r1:32b` | Qwen-2.5-32B Dense | 19.8 GB | ~22.4 GB | 24GB VRAM GPU (RTX 3090, RTX 4090, RX 7900 XTX) |
| `deepseek-r1:70b` | Llama-3.3-70B Dense | 42.5 GB | ~46.5 GB | 2× 24GB GPUs (48GB) or Apple Silicon 64GB+ Unified |
| `deepseek-r1:671b` | DeepSeekMoE (37B Active) | 404 GB (Q4) / 131 GB (1.58-bit) | 160 GB – 430 GB | Mac Studio 192GB/512GB or Multi-GPU + DDR5 Server (`ktransformers`) |

## Step-by-Step: Running DeepSeek R1 Locally with Ollama + Open WebUI

To get a ChatGPT-style interface that renders DeepSeek-R1’s collapsible `<think>...</think>` reasoning blocks cleanly on your local machine without sending a single token to external servers:

```
# Step 1: Pull your target DeepSeek-R1 model in Ollama (e.g., 14B for 12GB-16GB GPUs)
ollama pull deepseek-r1:14b

# Step 2: Create a custom Modelfile with DeepSeek's recommended 0.6 temperature (prevents repetition loops)
cat << 'EOF' > Modelfile.deepseek
FROM deepseek-r1:14b
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER num_ctx 16384
EOF

ollama create deepseek-r1-14b-tuned -f Modelfile.deepseek

# Step 3: Launch Open WebUI bound to localhost (127.0.0.1:3000)
docker run -d -p 127.0.0.1:3000:8080
  --add-host=host.docker.internal:host-gateway
  -v open-webui:/app/backend/data
  --name open-webui --restart always
  ghcr.io/open-webui/open-webui:main
```

Need to calculate exact memory footprints for custom context lengths (`32K` or `64K`) or compare other coding models? Use our [2026 Local LLM VRAM Calculator](https://theinfosiast.com/running-local-llms-consumer-hardware-2026/#ti-vram-calculator) and our [Best Local LLMs for 8GB, 12GB & 16GB VRAM Guide](https://theinfosiast.com/best-local-llms-8gb-12gb-16gb-vram/).

Written and prepared by [Kshitij Gupta](https://www.linkedin.com/in/kshitij-gupta-855535134/).