Bonus: Top up now and we'll double your first deposit — get x2 credits instantly.
Get Started
Sign Up
Sign Up

DeepSeek-R1 Distill Qwen 32B: Self-Hosting & Deployment Guide

DeepSeek-R1 Distill Qwen 32B is a reasoning model distilled from DeepSeek R1 onto a 32B Qwen backbone.

Browse available models

What is DeepSeek-R1 Distill Qwen 32B?

DeepSeek-R1 Distill Qwen 32B is a reasoning model distilled from DeepSeek R1 onto a 32B Qwen backbone, published by DeepSeek with open weights at deepseek-ai/DeepSeek-R1-Distill-Qwen-32B. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly.

How to run DeepSeek-R1 Distill Qwen 32B yourself

Open weights (mit), 32.8B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

DeepSeek-R1 Distill Qwen 32B VRAM requirements

Precision / quantizationWeightsFigureSource
BF16 (full precision)65.5 GBEstimate32.8B params × 2 bytes
FP834.3 GBFile sizeRedHatAI/DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic
AWQ 4-bit19.3 GBFile sizecasperhansen/deepseek-r1-distill-qwen-32b-awq
MLX 4-bit18.4 GBFile sizemlx-community/DeepSeek-R1-Distill-Qwen-32B-4bit
GGUF Q8_034.8 GBFile sizeunsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF
GGUF Q4_K_M19.9 GBFile sizeunsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF
GGUF Q2_K12.3 GBFile sizeunsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy DeepSeek-R1 Distill Qwen 32B on your hardware

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
NVIDIA H200141 GB1×1×1×1×1×1×1×
NVIDIA H10080 GB1×1×1×1×1×1×1×
NVIDIA A100 80GB80 GB1×1×1×1×1×1×1×
NVIDIA L40S48 GB2×1×1×1×1×1×1×

GPUs needed per variant.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
RTX 509032 GB2×1×1×
RTX 409024 GB2×1×1×
RTX 309024 GB2×1×1×

GGUF via llama.cpp; more cards or CPU offload for larger files.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_KMLX 4-bit
Mac, 512 GBM3 UltraFitsFitsFitsFits
Mac, 192 GBM2 UltraFitsFitsFitsFits
Mac, 128 GBM4 Max / M3 MaxFitsFitsFitsFits
Mac, 64 GBM4 Pro / MaxFitsFitsFitsFits

Assumes ~75% of unified memory is usable by the GPU.

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS p4de.24xlarge8× A100 80GB · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS g6e.12xlarge4× L40S · 192 GBFitsFitsFitsFitsFitsFitsFits
Google Cloud a3-highgpu-8g8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
Azure ND H100 v58× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits

Whole-instance GPU memory; public instance specs.

Serve it yourself

pip install -U vllm
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
pip install 'sglang[all]'
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
llama-server -hf unsloth/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99
pip install -U mlx-lm
mlx_lm.generate --model mlx-community/DeepSeek-R1-Distill-Qwen-32B-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Not on LLM.API yet

We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Not available on LLM API yet

We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Why run DeepSeek-R1 Distill Qwen 32B on LLM.API?

  • Unified AI Routing

    Reach DeepSeek-R1 Distill Qwen 32B and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale DeepSeek-R1 Distill Qwen 32B.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for DeepSeek-R1 Distill Qwen 32B alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap DeepSeek-R1 Distill Qwen 32B for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Get one key to every model

Swap your API key. Keep your code.

Browse available models Talk to sales