Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for DeepSeek R1 0528 Qwen3 8B?

Not always. MacBook Pro M3 Max 128GB 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 MacBook Pro M3 Max 128GB?

YES — Runs Great

C45Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~20.5 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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) — 20.5 GB, 49.2 tok/s, Runs well

20.5 GB required92.2 GB available

22% VRAM used

Fit status

Runs well

Decode

49.2 tok/s

TTFT

3937 ms

Safe context

1.2M

Memory

20.5 GB / 92.2 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on MacBook Pro M3 Max 128GB
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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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	49.2 tok/s	2147 ms	1.2M
Coding	C	Runs well	49.2 tok/s	3937 ms	1.2M
Agentic Coding	C	Runs well	49.2 tok/s	5726 ms	1.2M
Reasoning	C	Runs well	49.2 tok/s	4652 ms	1.2M
RAG	C	Runs well	49.2 tok/s	7157 ms	1.2M

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	D39
Q3_K_S	3	3.9 GB	Low	D39
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs DeepSeek R1 0528 Qwen3 8B well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 128%.112 tok/s decode

Raises estimated decode speed by about 128%.

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

~$6,999 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBBest value

1792 GB/s (+1392)

Raises estimated decode speed by about 128%.112 tok/s decode

Raises estimated decode speed by about 128%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run DeepSeek R1 0528 Qwen3 8B?

Yes, MacBook Pro M3 Max 128GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 49.2 tok/s.

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

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 20.5 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 MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3937ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on MacBook Pro M3 Max 128GB receives a C grade with 49.2 tok/s and 1.2M context.

What context window can DeepSeek R1 0528 Qwen3 8B use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 128GB See all hardware for DeepSeek R1 0528 Qwen3 8B

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