Can DeepSeek R1 0528 Qwen3 8B run on MacBook Pro M1 Max 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~10.2 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~45 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) — 10.2 GB, 45.1 tok/s, Runs well

10.2 GB required23.0 GB available

44% VRAM used

Fit status

Runs well

Decode

45.1 tok/s

TTFT

4294 ms

Safe context

236K

Memory

10.2 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on MacBook Pro M1 Max 32GB
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: 45.1 tok/s decode · 4.3s TTFT (warm) · 113 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	45.1 tok/s	2342 ms	236K
Coding	C	Runs well	45.1 tok/s	4294 ms	236K
Agentic Coding	C	Runs well	45.1 tok/s	6246 ms	236K
Reasoning	C	Runs well	45.1 tok/s	5075 ms	236K
RAG	C	Runs well	45.1 tok/s	7808 ms	236K

Inference speed

DeepSeek R1 0528 Qwen3 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 0528 Qwen3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C46
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C47
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

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

Opções de upgrade

Hardware que roda bem DeepSeek R1 0528 Qwen3 8B

RX 7900 XTX 24GBMelhor custo-benefício

960 GB/s (+560)

Raises estimated decode speed by about 148%.112 tok/s decodificação

Raises estimated decode speed by about 148%.

~$999 MSRP

MacBook Pro M4 Max 36GBOpção econômica

36 GB Unified (+4)410 GB/s (+10)

Raises estimated decode speed by about 28%.57.7 tok/s decodificação

Raises estimated decode speed by about 28%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M1 Max 32GB run DeepSeek R1 0528 Qwen3 8B?

Yes, MacBook Pro M1 Max 32GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 45.1 tok/s.

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

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 10.2 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 M1 Max 32GB?

On MacBook Pro M1 Max 32GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 45.1 tokens per second decode speed with a time-to-first-token of 4294ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 32GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on MacBook Pro M1 Max 32GB receives a C grade with 45.1 tok/s and 236K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Max 32GB as fast as VRAM for DeepSeek R1 0528 Qwen3 8B?

Not always. MacBook Pro M1 Max 32GB 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.

See all results for MacBook Pro M1 Max 32GB See all hardware for DeepSeek R1 0528 Qwen3 8B

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