text model · DeepSeek-R1-Distill · macOS

Can I run DeepSeek-R1-0528-Qwen3-8B on Apple M5 (16GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~26 tok/s est.

Yes. DeepSeek-R1-0528-Qwen3-8B runs on Apple M5 (16GB) at Q4_K_M (~6.2 GB of ~10.5 GB usable).

Needs ~6.2 GB Device usable ~10.5 GB

Runs at Q4_K_M using ~6.2 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 (16GB) leaves ~4.3 GB of headroom, room to step up to Q8_0 for higher quality.

Q4_K_M needed: ~6.2 GB
Usable on device: ~10.5 GB
Device memory: 16 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~4.9 GB

Q3_K_M

~5.5 GB

Q4_K_M

~6.2 GB

Q5_K_M

~7.3 GB

Q6_K

~8.2 GB

Q8_0

~9.6 GB

FP16

~17.9 GB

The line marks Apple M5 (16GB)'s ~10.5 GB budget; rungs past it are too large.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run deepseek-r1:8b-0528-qwen3-q4_K_M

llama.cpp

$ llama-cli -hf unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF

Model DeepSeek-R1-Distill

Parameters: 8.19B
Q4_K_M size: 4.68 GB
Q8_0 size: 8.11 GB
Context: 128k
Ollama tag: deepseek-r1:8b-0528-qwen3-q4_K_M

Full DeepSeek-R1-0528-Qwen3-8B requirements →

Device macOS

Memory: 16 GB unified
Usable for weights: ~10.5 GB
Best runtime: MLX direct / Ollama (MLX backend)

Best models for Apple M5 (16GB) →

You could also run

Run DeepSeek-R1-0528-Qwen3-8B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Samsung Galaxy S24 Ultra Generic Android Phone (12GB RAM)iPhone 17 Pro iPhone Air

FAQ

Can Apple M5 (16GB) run DeepSeek-R1-0528-Qwen3-8B?

Yes. DeepSeek-R1-0528-Qwen3-8B runs on Apple M5 (16GB) at Q4_K_M (~6.2 GB of ~10.5 GB usable).

How much memory does DeepSeek-R1-0528-Qwen3-8B need?

Apple M5 (16GB) has room to spare. At Q4_K_M the weights are ~4.68 GB; with KV cache and runtime overhead, budget ~6.2 GB at a 4k context.

What is the best tool to run DeepSeek-R1-0528-Qwen3-8B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

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Markdown

[![DeepSeek-R1-0528-Qwen3-8B on Apple M5 (16GB)](https://localmodel.run/badge/deepseek-r1-0528-qwen3-8b/apple-m5-16gb.svg)](https://localmodel.run/can-i-run/deepseek-r1-0528-qwen3-8b/apple-m5-16gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.