Is unified memory on Mac mini M2 24GB as fast as VRAM for DeepSeek R1 0528 Qwen3 8B?

Not always. Mac mini M2 24GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

Can DeepSeek R1 0528 Qwen3 8B run on Mac mini M2 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~9.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.3 GB, 13.3 tok/s, Runs well

9.3 GB required17.3 GB available

54% VRAM used

Fit status

Runs well

Decode

13.3 tok/s

TTFT

14535 ms

Safe context

152K

Memory

9.3 GB / 17.3 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on Mac mini M2 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 13.3 tok/s decode · 14.5s TTFT (warm) · 33 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	13.3 tok/s	7928 ms	152K
Coding	C	Runs well	13.3 tok/s	14535 ms	152K
Agentic Coding	C	Runs well	13.3 tok/s	21142 ms	152K
Reasoning	C	Runs well	13.3 tok/s	17178 ms	152K
RAG	C	Runs well	13.3 tok/s	26427 ms	152K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.

Run

lms load hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 0528 Qwen3 8B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 257%.47.5 tok/s decode

Raises estimated decode speed by about 257%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 116%.28.7 tok/s decode

Raises estimated decode speed by about 116%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Raises estimated decode speed by about 615%.95.1 tok/s decode

Raises estimated decode speed by about 615%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run DeepSeek R1 0528 Qwen3 8B?

Yes, Mac mini M2 24GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 13.3 tok/s.

How much VRAM does DeepSeek R1 0528 Qwen3 8B need?

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 0528 Qwen3 8B?

The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 0528 Qwen3 8B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14535ms using Q4_K_M quantization.

Can Mac mini M2 24GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on Mac mini M2 24GB receives a C grade with 13.3 tok/s and 152K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on Mac mini M2 24GB?

On Mac mini M2 24GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac mini M2 24GB See all hardware for DeepSeek R1 0528 Qwen3 8B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: