Skip to main content
Last verified: 2026-10-14
DeepSeek-R1 is DeepSeek’s earlier reasoning family. The way almost everyone runs it locally is a distill — a smaller Qwen or Llama model fine-tuned on R1’s reasoning traces, so the reasoning behavior runs on a laptop-class GPU instead of a multi-GPU server. Use this page when you need that smaller hardware path. For DeepSeek’s current open-weight release, coding-agent improvements, tool calling, and 1M context, start with DeepSeek-V4-Flash-0731. Pair Tokios with whichever model you run to reach it from any machine, teammate, or hosted agent through https://api.tokios.com — no inbound ports, no VPN.

​
Run it locally with Ollama

The deepseek-r1 tag on Ollama ships the distills, not the full model. Pull and run the default (currently the 7b Qwen distill):
For a specific size, name the tag directly — for example ollama run deepseek-r1:14b. See the Ollama library page for the full tag list.
Ollama’s default context window is 2048 tokens, far below what these models support — and R1-style reasoning burns through context fast on its way to an answer. Raise num_ctx (via /set parameter num_ctx <value> in the CLI, or in your Modelfile/API request) toward the context size listed in the table below.

​
Variants & tags

Every value below is from the Ollama library and each model’s Hugging Face config. Tags, sizes, and defaults change as new quantizations ship — confirm against ollama.com/library/deepseek-r1 before you standardize on one. Sources: ollama.com/library/deepseek-r1/tags, deepseek-ai/DeepSeek-R1 README.
The 671b tags are the full DeepSeek-R1 (and R1-0528), not distills — Ollama just publishes them under the same deepseek-r1 name. Everything from 1.5b through 70b is a distill: a Qwen or Llama model fine-tuned on R1’s reasoning traces, not R1 itself shrunk down. See how inference works for what that distinction means for output quality.

​
Quantization & formats

Ollama’s default tags are GGUF Q4_K_M. Every distill size and the full 671b also publish -q8_0 and -fp16 tags on the library page for less lossy (and much larger) downloads. See Quantization formats and the hardware they need for what runs on your GPU, and what quantization is for how to read the Q4_K_M-style names.

​
Hardware fit

Comfort at roughly Q4 for a modest context length. Treat every entry as an estimate — the ground truth is the actual GGUF size plus the KV cache for your context. See what size model fits your GPU? for the sizing math. The full 671b needs roughly 404 GB just for weights at Q4_K_M — that’s multiple datacenter-class GPUs or an equivalent unified-memory cluster, not a single workstation. Everything 70b and under is realistic on the hardware hobbyists actually own.

​
Run it — beginner to advanced

Leave Ollama listening on the default http://127.0.0.1:11434. Raise num_ctx to use more of the model’s context window.

​
Capabilities

  • Reasoning: R1 and every distill emit a <think>…</think> block before the final answer. DeepSeek’s own model card recommends a temperature of 0.5–0.7 (0.6 suggested), no system prompt, and — for math — explicitly asking the model to “reason step by step” and box its final answer.
  • Native tool-calling: Not reliable on the distills. Ollama’s chat template for the R1 distills doesn’t support tool/function-calling the way Llama 3.1/3.3 or Qwen3 do — see ollama/ollama#8517 (closed, unresolved for the distill template). If your workload needs an agent that calls tools, pick a model built for it — see Choose a model by task.
  • Context: 128K for every distill (1.5b through 70b); 160K for the full 671b R1 and R1-0528.
  • Vision: None of the models on this page are multimodal — DeepSeek-R1 and its distills are text-only.

​
Beyond R1: use V4 Flash 0731 for the current frontier

R1 is a reasoning-tuned checkpoint built on DeepSeek’s V3 architecture. The current official release is deepseek-ai/DeepSeek-V4-Flash-0731, which supersedes the V4 Flash preview. It is open-weight and MIT-licensed, but it needs a multi-device server rather than a typical single workstation. The DeepSeek family overview covers its 284B-target / 13B-active architecture, bundled DSpark head, tool calling, 1M context, and local hardware paths.

​
Connect it with Tokios

Once a DeepSeek model is running locally, pair the Tokios connector so you can reach it as https://api.tokios.com from Claude Code, Cline, or any other compatible client — instead of only from localhost:11434.
1

Pair the Tokios connector

Sign in at tokios.com/console, open the Setup tab, and click Start pairing to get a one-time claim code (TKS-XXXX-XXXX). Install and run the connector on the machine running Ollama:
Windows
macOS
Linux
Then approve the device on the Setup or Connectors tab.
2

Point the connector at Ollama's /v1 API

Ollama exposes an OpenAI-compatible surface at /v1. For a single-model setup, the console’s Setup wizard fills this in automatically as http://127.0.0.1:11434/v1. For multiple models, set it explicitly in tokios-connector.json — see Connector config.
3

Register a deployment

In the Models tab, register the model and choose a public deployment name, for example deepseek-r1-tunnel. This name — not Ollama’s local model id — is what clients send in the model field.
4

Create an API key

In the Keys tab, click Create key. Copy the sk-tok-… key now — it’s shown only once.

​
Call it from anywhere

The model field is always your registered deployment name (deepseek-r1-tunnel above), never Ollama’s local model id. Tokios turns whatever’s running on localhost into a stable, authenticated endpoint — it doesn’t change what the model can do. See Use Claude Code and Use Cline for full client setup.

​
Caveats

  • Tool-calling is unreliable on the R1 distills. If you’re wiring up an agent, CLI, or IDE plugin that needs the model to call tools, use a model with confirmed native tool support instead — see Choose a model by task.
  • Raise the context window. Ollama’s 2048-token default cuts off R1-style reasoning output quickly; increase num_ctx for real use, up to 128K (distills) or 160K (full 671b).
  • A distill is not the full model. The 1.5b–70b tags are Qwen/Llama checkpoints fine-tuned on R1’s reasoning traces — they reproduce the reasoning behavior on light hardware, not R1’s full capability. Benchmark quality drops as size drops; see the DeepSeek-R1 model card for the distill evaluation table.
  • Licensing differs by distill. The full R1/R1-0528 weights and the Qwen-based distills (1.5b, 7b, 14b, 32b) are MIT. The Llama-based distills (8b, 70b) carry Meta’s Llama 3.1/3.3 license instead, inherited from their base model — check that license before commercial use of those two specific tags.
  • 671B needs real multi-GPU hardware — roughly 404 GB at Q4_K_M alone — not a typical single-workstation setup. The current V4 Flash 0731 checkpoint is smaller but still needs roughly 162–167 GB before KV cache and runtime overhead; see the DeepSeek family overview.
  • Tokios passes tool-calling requests straight through to whatever the upstream model supports — it doesn’t add tool-calling capability the underlying model lacks.

DeepSeek family overview

R1 versus V3.x versus the V4 frontier — which tier fits your hardware.

Choose a model by task

Pick a model with reliable tool-calling if you’re building an agent.

Match a model to your GPU

Compare VRAM classes across distill sizes before you pick a tag.

Quantization & hardware

Which formats — GGUF, FP16, and beyond — run on your hardware.