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URL: https://willitrunai.com/can-run/hf-maziyarpanahi--deepseek-r1-0528-qwen3-8b-gguf-on-m2-max-32gb

⇱ DeepSeek R1 0528 Qwen3 8B on MacBook Pro M2 Max 32GB? YES


Can DeepSeek R1 0528 Qwen3 8B run on MacBook Pro M2 Max 32GB?

YES — Runs Great

C49Usable
Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~10.2 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

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

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, 47.5 tok/s, Runs well
10.2 GB required23.0 GB available
44% VRAM used

Fit status

Runs well

Decode

47.5 tok/s

TTFT

4072 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 M2 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: 47.5 tok/s decode · 4.1s TTFT (warm) · 119 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well47.5 tok/s2221 ms236K
CodingCRuns well47.5 tok/s4072 ms236K
Agentic CodingCRuns well47.5 tok/s5923 ms236K
ReasoningCRuns well47.5 tok/s4813 ms236K
RAGCRuns well47.5 tok/s7404 ms236K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC45
Q3_K_S
3
3.9 GB
LowC45
NVFP4
4
4.5 GB
MediumC46
Q4_K_M
4
4.9 GB
MediumC46
Q5_K_M
5
5.8 GB
HighC46
Q6_K
6
6.6 GB
HighC47
Q8_0
8
8.6 GB
Very HighC48
F16Best for your GPU
16
16.4 GB
MaximumC50

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

RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
C
Raises estimated decode speed by about 136%.112 tok/s decode

Raises estimated decode speed by about 136%.

~$999 MSRP

👁 NVIDIA
RTX 3090 24GBBest value
936 GB/s (+536)
C
Raises estimated decode speed by about 102%.96 tok/s decode

Raises estimated decode speed by about 102%.

~$1,499 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Max 32GBSee all hardware for DeepSeek R1 0528 Qwen3 8B