text model · DeepSeek-R1-Distill · macOS

Can I run DeepSeek-R1-0528-Qwen3-8B on Apple M1 (8GB)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. DeepSeek-R1-0528-Qwen3-8B needs ~6.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

Needs ~6.2 GB Device usable ~5.5 GB

Needs ~6.2 GB even at Q4_K_M, but only ~5.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 0.7 GB: DeepSeek-R1-0528-Qwen3-8B needs roughly 6.2 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs DeepSeek-R1-0528-Qwen3-8B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See DeepSeek-R1-0528-Qwen3-8B on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed: ~6.2 GB
Usable on device: ~5.5 GB
Device memory: 8 GB

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Which quant fits

Quant ladder vs ~5.5 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 M1 (8GB)'s ~5.5 GB budget; rungs past it are too large.

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: 8 GB unified
Usable for weights: ~5.5 GB
Power draw: ~39 W
Best runtime: Ollama (llama.cpp Metal backend)

Best models for Apple M1 (8GB) →

What you can run instead

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 M1 (8GB) run DeepSeek-R1-0528-Qwen3-8B?

No. DeepSeek-R1-0528-Qwen3-8B needs ~6.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.

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

Apple M1 (8GB) does not have enough memory. 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 M1 (8GB)](https://localmodel.run/badge/deepseek-r1-0528-qwen3-8b/apple-m1-8gb.svg)](https://localmodel.run/can-i-run/deepseek-r1-0528-qwen3-8b/apple-m1-8gb)

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.