Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for DeepSeek R1 Distill Llama 8B?

Not always. MacBook Pro M2 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.

Can DeepSeek R1 Distill Llama 8B run on MacBook Pro M2 Max 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

DeepSeek R1 Distill Llama 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

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 Distill Llama 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	47.5 tok/s	2221 ms	236K
Coding	C	Runs well	47.5 tok/s	4072 ms	236K
Agentic Coding	C	Runs well	47.5 tok/s	5923 ms	236K
Reasoning	C	Runs well	47.5 tok/s	4813 ms	236K
RAG	C	Runs well	47.5 tok/s	7404 ms	236K

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on MacBook Pro M2 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

Get started

Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Llama 8B well

RX 7900 XTX 24GBBudget pick

960 GB/s (+560)

Raises estimated decode speed by about 136%.112 tok/s decode

Raises estimated decode speed by about 136%.

~$999 MSRP

RTX 3090 24GBBest value

936 GB/s (+536)

Raises estimated decode speed by about 102%.96 tok/s decode

Raises estimated decode speed by about 102%.

~$1,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 32GB run DeepSeek R1 Distill Llama 8B?

Yes, MacBook Pro M2 Max 32GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs well). Expected decode speed: 47.5 tok/s.

How much VRAM does DeepSeek R1 Distill Llama 8B need?

DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Llama 8B?

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

What speed will DeepSeek R1 Distill Llama 8B run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, DeepSeek R1 Distill Llama 8B achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4072ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on MacBook Pro M2 Max 32GB receives a C grade with 47.5 tok/s and 236K context.

What context window can DeepSeek R1 Distill Llama 8B use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, DeepSeek R1 Distill Llama 8B can safely use up to 236K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Max 32GB See all hardware for DeepSeek R1 Distill Llama 8B

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