Choosing the right DeepSeek R1 size
DeepSeek R1 comes in several distilled variants based on Qwen and Llama architectures. The 7B distill runs on 8 GB VRAMVRAMVideo Random Access Memory (VRAM) is the dedicated memory on graphics cards that stores model weights and the KV cache during LLM inference.Learn more → at Q4 quantisation (5 GB model size), making it accessible on most gaming GPUs and MacBook Pros with 8 GB unified memory. The 14B distill delivers meaningfully better reasoning and needs around 10 GB VRAM.
For serious reasoning workloads — maths, coding, multi-step logic — the 70B distill is the sweet spot if you have 40+ GB of unified memory (M2 Max/Ultra, M3 Max) or a workstation with dual GPUs. The full R1 671B MoE model requires server-grade hardware and is best accessed via API.
Hardware requirements at a glance
DeepSeek R1 7B: 8 GB RAM/VRAM minimum, 12 GB recommended. Runs well on an M1/M2 MacBook Pro 8 GB or an RTX 3060 12 GB.
DeepSeek R1 14B: 12 GB VRAM minimum, 16 GB recommended. Works on an M2 Pro 16 GB or an RTX 3090/4070 Ti.
DeepSeek R1 70B: 40 GB unified memory (M2/M3 Max) or dual RTX 3090 (48 GB VRAM total). At Q4_K_M quantisation the model file is around 40 GB.
Run with Ollama (recommended)
Install Ollama first if you haven't already (see our Ollama installation guide). Then in a terminal run: `ollama pull deepseek-r1:7b` for the 7B variant, or `ollama pull deepseek-r1:14b` for the 14B.
Start an interactive session: `ollama run deepseek-r1:14b`. DeepSeek R1 models emit chain-of-thought `<think>` blocks before their final answer — this is normal. The thinking section can be several paragraphs long for complex problems. The actual answer follows after the closing `</think>` tag.
To suppress the thinking output in the Ollama CLI, add `--nostream` and pipe through a quick filter, or use the API endpoint and strip the think tags programmatically.
Run with LM Studio
Open LM Studio, search for 'DeepSeek R1' in the model browser, and select the appropriate size. The recommended quantisation is Q4_K_M for the best quality-to-size ratio. Download and load the model.
In the system promptSystem PromptA special instruction given to a language model before the user conversation begins, establishing the model's persona, capabilities, constraints, and context.Learn more → field, you can optionally add: 'You are a helpful AI assistant. Think through problems step by step before giving your final answer.' This primes the model to use its reasoning capability fully.
Tips for best results
DeepSeek R1 excels at tasks with clear correct/incorrect answers: maths, logic puzzles, coding, and structured data extraction. Frame your prompts precisely — ambiguous prompts lead to unnecessary hedging in the thinking section.
For coding tasks, always include the programming language and any relevant context (existing functions, error messages, or expected behaviour). The model tends to write more idiomatic code when given clear examples of your coding style.
If responses feel slow, reduce the context length in the model settings. The default 32K–64K context loads more KV cacheKV CacheA memory optimization that stores previously computed attention keys and values so the model doesn't recompute them when generating each new token.Learn more → memory than most tasks need — for simple questions, 4K context is enough and will be 2–3× faster on CPU offload.