DeepSeek-R1 Reasoning Model

Run DeepSeek-R1 open-source reasoning model on Clore.ai GPUs

All examples run on GPU servers rented via the CLORE.AI Marketplace. RTX 4090 instances start at ~$0.50/day.

Overview

DeepSeek-R1 is a 671B-parameter open-weight reasoning model released in January 2025 by DeepSeek under the Apache 2.0 license. It is the first open model to match OpenAI o1 across math, coding, and scientific benchmarks — while exposing its entire chain-of-thought through explicit <think> tags.

The full model uses Mixture-of-Experts (MoE) with 37B active parameters per token, making inference tractable despite the headline parameter count. For most practitioners, the distilled variants (1.5B → 70B) are more practical: they inherit R1's reasoning patterns through knowledge distillation into Qwen-2.5 and Llama-3 base architectures and run on commodity GPUs.

Key Features

• Explicit chain-of-thought — every response begins with a <think> block where the model reasons, backtracks, and self-corrects before producing a final answer

• Reinforcement-learning trained — reasoning ability emerges from RL reward signals rather than hand-authored chain-of-thought data

• Six distilled variants — 1.5B, 7B, 8B, 14B, 32B, 70B parameter models distilled from the full 671B into Qwen and Llama architectures

• Apache 2.0 license — fully commercial, no royalties, no usage restrictions

• Wide framework support — Ollama, vLLM, llama.cpp, SGLang, Transformers, TGI all work out of the box

• AIME 2024 Pass@1: 79.8% — ties with OpenAI o1 on competition math

• Codeforces 2029 Elo — exceeds o1's 1891 on competitive programming

Model Variants

Variant	Parameters	Architecture	FP16 VRAM	Q4 VRAM	Q4 Disk
DeepSeek-R1 (full MoE)	671B (37B active)	DeepSeek MoE	~1.3 TB	~350 GB	~340 GB
R1-Distill-Llama-70B	70B	Llama 3	140 GB	40 GB	42 GB
R1-Distill-Qwen-32B	32B	Qwen 2.5	64 GB	22 GB	20 GB
R1-Distill-Qwen-14B	14B	Qwen 2.5	28 GB	10 GB	9 GB
R1-Distill-Llama-8B	8B	Llama 3	16 GB	6 GB	5.5 GB
R1-Distill-Qwen-7B	7B	Qwen 2.5	14 GB	5 GB	4.5 GB
R1-Distill-Qwen-1.5B	1.5B	Qwen 2.5	3 GB	2 GB	1.2 GB

Choosing a Variant

Use Case	Recommended Variant	GPU on Clore
Quick experiments, edge testing	R1-Distill-Qwen-1.5B	Any GPU
Budget deployment, fast inference	R1-Distill-Qwen-7B	RTX 3090 (~$0.30–1/day)
Single-GPU production sweet spot	R1-Distill-Qwen-14B Q4	RTX 4090 (~$0.50–2/day)
Best quality-per-dollar (recommended)	R1-Distill-Qwen-32B Q4	RTX 4090 24 GB or A100 40 GB
Maximum distilled quality	R1-Distill-Llama-70B	2× A100 80 GB
Research, full-fidelity reasoning	DeepSeek-R1 671B	8× H100 cluster

HuggingFace Repositories

Variant	Repository
Full R1	deepseek-ai/DeepSeek-R1
Llama-70B distill	deepseek-ai/DeepSeek-R1-Distill-Llama-70B
Qwen-32B distill	deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
Qwen-14B distill	deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Llama-8B distill	deepseek-ai/DeepSeek-R1-Distill-Llama-8B
Qwen-7B distill	deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Qwen-1.5B distill	deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Requirements

Component	Minimum (7B Q4)	Recommended (32B Q4)
GPU VRAM	6 GB	24 GB
System RAM	16 GB	32 GB
Disk	10 GB	30 GB
CUDA	12.1+	12.4+
Docker	24.0+	25.0+

Ollama Quick Start

Ollama handles quantization, downloading, and serving automatically — the fastest path to a running DeepSeek-R1.

Install and run

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull and run (pick your variant):
ollama run deepseek-r1:1.5b    # Edge / testing
ollama run deepseek-r1:7b      # Budget — fits any 8 GB+ GPU
ollama run deepseek-r1:14b     # RTX 4090 sweet spot
ollama run deepseek-r1:32b     # A100 / 2× RTX 4090
ollama run deepseek-r1:70b     # 2× A100 80 GB

Example interactive session

>>> Prove that √2 is irrational.

<think>
I'll use proof by contradiction.
Assume √2 is rational, so √2 = p/q where p,q are integers with gcd(p,q) = 1.
Then 2 = p²/q², which gives p² = 2q².
This means p² is even, so p must be even. Write p = 2k.
Substituting: (2k)² = 2q² → 4k² = 2q² → q² = 2k².
So q² is also even, meaning q is even.
But if both p and q are even, gcd(p,q) ≥ 2, contradicting our assumption.
</think>

**Proof that √2 is irrational (by contradiction):**

Assume √2 = p/q in lowest terms (gcd(p,q) = 1). Squaring gives p² = 2q².
Since p² is even, p is even — write p = 2k. Then 4k² = 2q², so q² = 2k²,
meaning q is also even. This contradicts gcd(p,q) = 1. ∎

Use the OpenAI-compatible API

# Start Ollama as a server (if not already running)
ollama serve &

# Query via curl
curl -s http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1:32b",
    "messages": [{"role": "user", "content": "Factor x^4 - 1 completely over the integers."}],
    "temperature": 0.6
  }' | python3 -m json.tool

Python client (via OpenAI SDK)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

response = client.chat.completions.create(
    model="deepseek-r1:32b",
    messages=[
        {"role": "user", "content": "Write a Python function to find the longest palindromic substring."}
    ],
    temperature=0.6,
    max_tokens=4096,
)
print(response.choices[0].message.content)

vLLM Production Setup

vLLM delivers the highest throughput for multi-user serving with continuous batching, PagedAttention, and prefix caching.

Single GPU — 7B / 14B

pip install vllm

# 7B on any 16 GB+ GPU
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B \
    --host 0.0.0.0 --port 8000 \
    --max-model-len 16384

# 14B on RTX 4090 (24 GB)
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-14B \
    --host 0.0.0.0 --port 8000 \
    --max-model-len 16384 \
    --gpu-memory-utilization 0.92

Multi-GPU — 32B (recommended)

vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
    --host 0.0.0.0 --port 8000 \
    --tensor-parallel-size 2 \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.90 \
    --enable-prefix-caching

Tip: The 32B Q4 GPTQ or AWQ checkpoint fits on a single RTX 4090 (24 GB):

vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
    --quantization awq --host 0.0.0.0 --port 8000 \
    --max-model-len 16384

Multi-GPU — 70B

vllm serve deepseek-ai/DeepSeek-R1-Distill-Llama-70B \
    --host 0.0.0.0 --port 8000 \
    --tensor-parallel-size 4 \
    --max-model-len 32768 \
    --gpu-memory-utilization 0.90

Query the vLLM endpoint

curl -s http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
    "messages": [{"role": "user", "content": "Solve: find all primes p such that p^2 + 2 is also prime."}],
    "temperature": 0.6,
    "max_tokens": 4096
  }'

Transformers / Python (with <think> Tag Parsing)

Use HuggingFace Transformers when you need fine-grained control over generation or want to integrate R1 into a Python pipeline.

Basic generation

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, re

MODEL = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"

tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "What is the sum of the first 100 positive integers?"
messages = [{"role": "user", "content": prompt}]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=2048,
        temperature=0.6,
        do_sample=True,
    )

full_response = tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(full_response)

Parsing <think> tags

def parse_r1_response(text: str) -> dict:
    """Split a DeepSeek-R1 response into thinking and answer parts."""
    think_match = re.search(r"<think>(.*?)</think>", text, re.DOTALL)
    thinking = think_match.group(1).strip() if think_match else ""
    answer = re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip()
    return {
        "thinking": thinking,
        "answer": answer,
        "thinking_tokens": len(thinking.split()),
    }

result = parse_r1_response(full_response)
print(f"Model reasoned for {result['thinking_tokens']} words")
print(f"Answer: {result['answer']}")

Streaming with <think> state tracking

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")

stream = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
    messages=[{"role": "user", "content": "Derive the quadratic formula from ax² + bx + c = 0."}],
    stream=True,
    max_tokens=4096,
    temperature=0.6,
)

in_think = False
for chunk in stream:
    token = chunk.choices[0].delta.content or ""
    if "<think>" in token:
        in_think = True
        print("[Reasoning] ", end="", flush=True)
        continue
    if "</think>" in token:
        in_think = False
        print("\n[Answer] ", end="", flush=True)
        continue
    if not in_think:
        print(token, end="", flush=True)
print()

Docker Deployment on Clore.ai

Ollama Docker (simplest)

Docker image: ollama/ollama Ports: 22/tcp, 11434/http

# On the Clore instance
docker run -d --gpus all \
    -v ollama_data:/root/.ollama \
    -p 11434:11434 \
    --name deepseek-r1 \
    ollama/ollama

# Pull and serve the model
docker exec deepseek-r1 ollama pull deepseek-r1:32b

vLLM Docker (production)

Docker image: vllm/vllm-openai:latest Ports: 22/tcp, 8000/http

# docker-compose.yml
version: "3.8"
services:
  deepseek-r1:
    image: vllm/vllm-openai:latest
    ports:
      - "8000:8000"
    volumes:
      - hf_cache:/root/.cache/huggingface
    environment:
      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
    command: >
      --model deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
      --host 0.0.0.0 --port 8000
      --tensor-parallel-size 2
      --max-model-len 32768
      --gpu-memory-utilization 0.90
      --enable-prefix-caching
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 300s
volumes:
  hf_cache:

Deploy on Clore.ai:

1. Open clore.ai/marketplace

2. Filter by 2× GPU, 48 GB+ VRAM total (e.g. 2× RTX 4090 or A100 80 GB)

3. Set the Docker image to vllm/vllm-openai:latest

4. Map port 8000 as HTTP

5. Paste the command from the compose file above into the startup command

6. Connect via the HTTP endpoint once the health check passes

Tips for Clore.ai Deployments

Choosing the right GPU

Budget	GPU	Daily Cost	Best Variant
Minimal	RTX 3090 (24 GB)	$0.30 – 1.00	R1-Distill-Qwen-7B or 14B Q4
Standard	RTX 4090 (24 GB)	$0.50 – 2.00	R1-Distill-Qwen-14B FP16 or 32B Q4
Production	A100 80 GB	$3 – 8	R1-Distill-Qwen-32B FP16
High quality	2× A100 80 GB	$6 – 16	R1-Distill-Llama-70B FP16

Performance tuning

• Temperature 0.6 is the recommended default for reasoning tasks — DeepSeek's own papers use this value

• Set max_tokens generously — reasoning models produce long <think> blocks; 4096+ for non-trivial problems

• Enable prefix caching (--enable-prefix-caching in vLLM) when using a shared system prompt

• Limit concurrency (--max-num-seqs 16) for reasoning workloads — each request uses more compute than a standard chat

• Use Q4 quantization to fit 32B on a single 24 GB GPU with minimal quality loss (the distill already compresses R1's knowledge)

Context length considerations

Reasoning models consume more context than standard chat models because of the <think> block:

Task Complexity	Typical Thinking Length	Total Context Needed
Simple arithmetic	~100 tokens	~300 tokens
Code generation	~500–1000 tokens	~2000 tokens
Competition math (AIME)	~2000–4000 tokens	~5000 tokens
Multi-step research analysis	~4000–8000 tokens	~10000 tokens

Troubleshooting

Out of memory (OOM)

# Reduce context length
--max-model-len 8192    # instead of 32768

# Limit concurrent sequences
--max-num-seqs 8

# Use quantization
--quantization awq      # or gptq

Model produces no <think> block

Some system prompts suppress thinking. Avoid instructions like "be concise" or "don't explain your reasoning." Use a minimal system prompt or none at all:

# Good — preserves reasoning
messages = [{"role": "user", "content": "..."}]

# Bad — may suppress thinking
messages = [
    {"role": "system", "content": "Be extremely brief. No explanations."},
    {"role": "user", "content": "..."}
]

Repetitive or looping <think> output

Lower the temperature to reduce randomness in the reasoning chain:

temperature = 0.0   # Deterministic — best for math/code
temperature = 0.3   # Slight variation — good for analysis

Slow first token (high TTFT)

This is expected — the model generates <think> tokens before the visible answer. For latency-sensitive applications where reasoning is not needed, use DeepSeek-V3 instead.

Download stalls on Clore instance

HuggingFace downloads can be slow on some providers. Pre-cache the model into a persistent volume:

# Download once into a volume
huggingface-cli download deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
    --local-dir /data/models/deepseek-r1-32b

# Point vLLM at local path
vllm serve /data/models/deepseek-r1-32b --host 0.0.0.0 --port 8000

Further Reading

• DeepSeek-R1 Paper — Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

• DeepSeek-R1 GitHub — Official repository with model cards

• DeepSeek-V3 Guide — Non-reasoning general-purpose model from the same lab

• vLLM Guide — Comprehensive production serving setup

• Ollama Guide — Simple local deployment for any model

• Open WebUI Guide — Chat UI with native <think> tag rendering

• Qwen 2.5 Guide — The base architecture used by most R1 distills