DeepSeek-R1-Distill-Llama-8B-NexaQuant

Introduction

DeepSeek-R1 has been making headlines for rivaling OpenAI’s O1 reasoning model while remaining fully open-source. Many users want to run it locally to ensure data privacy, reduce latency, and maintain offline access. However, fitting such a large model onto personal devices typically requires quantization (e.g. Q4_K_M), which often sacrifices accuracy (up to ~22% accuracy loss) and undermines the benefits of the local reasoning model.

We’ve solved the trade-off by quantizing the DeepSeek R1 Distilled model to one-fourth its original file size—without losing any accuracy. Tests on an HP Omnibook AIPC with an AMD Ryzen™ AI 9 HX 370 processor showed a decoding speed of 17.20 tokens per second and a peak RAM usage of just 5017 MB in NexaQuant version—compared to only 5.30 tokens per second and 15564 MB RAM in the unquantized version—while NexaQuant maintaining full precision model accuracy.

NexaQuant Use Case Demo

Here’s a comparison of how a standard Q4_K_M and NexaQuant-4Bit handle a common investment banking brain teaser question. NexaQuant excels in accuracy while shrinking the model file size by 4 times.

Prompt: A Common Investment Banking BrainTeaser Question

A stick is broken into 3 parts, by choosing 2 points randomly along its length. With what probability can it form a triangle?

Right Answer: ¹⁄₄

Benchmarks

The benchmarks show that NexaQuant’s 4-bit model preserves the reasoning capacity of the original 16-bit model, delivering uncompromised performance in a significantly smaller memory & storage footprint.

Reasoning Capacity:

General Capacity:

Benchmark	Full 16-bit	llama.cpp (4-bit)	NexaQuant (4-bit)
HellaSwag	57.07	52.12	54.56
MMLU	55.59	52.82	54.94
ARC Easy	74.49	69.32	71.72
MathQA	35.34	30.00	32.46
PIQA	78.56	76.09	77.68
IFEval	36.26	35.35	34.12

Run locally

NexaQuant is compatible with Nexa-SDK, Ollama, LM Studio, Llama.cpp, and any llama.cpp based project. Below, we outline multiple ways to run the model locally.

Option 1: Using Nexa SDK

Step 1: Install Nexa SDK

Follow the installation instructions in Nexa SDK’s GitHub repository.

Step 2: Run the model with Nexa

Execute the following command in your terminal:

nexa run DeepSeek-R1-Distill-Llama-8B-NexaQuant:q4_0

Option 2: Using llama.cpp

Step 1: Build llama.cpp on Your Device

Follow the “Building the project” instructions in the llama.cpp repository to build the project.

Step 2: Run the Model with llama.cpp

Once built, run llama-cli under <build_dir>/bin/:

./llama-cli \
    --model your/local/path/to/DeepSeek-R1-Distill-Llama-8B-NexaQuant \
    --prompt 'Provide step-by-step reasoning enclosed in <think> </think> tags, followed by the final answer enclosed in \boxed{} tags.' \

Option 3: Using LM Studio

Step 1: Download and Install LM Studio

Get the latest version from the official website.

Step 2: Load and Run the Model

In LM Studio’s top panel, search for and select NexaAIDev/DeepSeek-R1-Distill-Llama-8B-NexaQuant.
Click Download (if not already downloaded) and wait for the model to load.
Once loaded, go to the chat window and start a conversation. —

What’s next

This model is built for complex problem-solving, which is why it sometimes takes a long thinking process even for simple questions. We recognized this and are working on improving it in the next update.
Inference Nexa Quantized Deepseek-R1 distilled model on NPU

If you liked our work, feel free to ⭐Star Nexa’s GitHub Repo.

Interested in running DeepSeek R1 on your own devices with optimized CPU, GPU, and NPU acceleration or compressing your finetuned DeepSeek-Distill-R1? Let’s chat!

Blogs | Discord | X(Twitter)

Join Discord server for help and discussion.

DeepSeek R1 Distilled model to one-fourth its original file size—without losing any accuracy.

Models

Readme