Instructions to use ubergarm/DeepSeek-R1-0528-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/DeepSeek-R1-0528-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Use Docker

docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

LM Studio
Jan

vLLM

How to use ubergarm/DeepSeek-R1-0528-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/DeepSeek-R1-0528-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/DeepSeek-R1-0528-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Ollama
How to use ubergarm/DeepSeek-R1-0528-GGUF with Ollama:
```
ollama run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
```

Unsloth Studio

How to use ubergarm/DeepSeek-R1-0528-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/DeepSeek-R1-0528-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/DeepSeek-R1-0528-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/DeepSeek-R1-0528-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ubergarm/DeepSeek-R1-0528-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S
```

Lemonade

How to use ubergarm/DeepSeek-R1-0528-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/DeepSeek-R1-0528-GGUF:IQ1_S

Run and chat with the model

lemonade run user.DeepSeek-R1-0528-GGUF-IQ1_S

List all available models

lemonade list

Request for an IQ5 Quant

#19

by binahz - opened Oct 31, 2025

Discussion

binahz

Oct 31, 2025

Hey, I know you this model is kinda old news now, but imo, its still one of the best for intelligence, longer context performance, and a nice writing style. Could you please create an IQ5 quant for this, similar to the ones you made for Deepseek v3.1, which will make it perfect for 768GB systems with 24GB VRAM? The only other one that comes close by anikifoss seems to have been quanted in such way where moving any of the full layers to GPU heavily hampers inference performance...

ubergarm

Owner Nov 3, 2025

Heya, I checked and do still have access to my DeepSeek-R1-0528-bf16-safetensors/ files, but given this was one of my earlier models I didn't go back with the more recent recipes.

I guess you prefer the older model over the more recent DeepSeek-V3.1-GGUF or DeepSeek-V3.1-Terminus-GGUF versions?

Not sure I'll get to it, especially if newer models land this week. Also am doing some maintenance on the big remote rig atm.

Regarding @anikifoss 's recipes, iirc they have slightly larger routed expert layers and no imatrix by design. Not sure why offloading more layers would hurt your performance though as typically each additional routed expert layer offloading onto GPU helps very slightly token generation speeds. KTransformers had an issue where offloading extra layers messed up the CUDA graphs or something, but afaik with ik_llama.cpp (which I assume you're using?) should be fine. Though ik's fork moves pretty fast with many small optimizations landing in just the past week e.g. https://github.com/ikawrakow/ik_llama.cpp/pull/842 which you could try testing with and without -no-ooae etc but just random guessing on my part at the moment.

Finally, are you benchmarking with llama-sweep-bench for your comparisons of speed (it is the best way to visualize both PP and TG speeds across various kv-cache depths imo).

Cheers!

ubergarm

Owner Nov 3, 2025

•

edited Nov 3, 2025

@binahz

I just saw a possibly (or not) issue mentioning where adding routed experts onto GPU was hurting performance: https://github.com/ggml-org/llama.cpp/issues/16945#issuecomment-3478207201

Are you seeing the issue with "heavily hampers inference performance" on other quants too or just the big one by anikifoss?

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