Can DeepSeek LLM 7B run on MacBook Pro M2 Pro 32GB?

Q: Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for DeepSeek LLM 7B?

Not always. MacBook Pro M2 Pro 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.

YES — Runs Great

C52Usable

Estimated from fit model

DeepSeek LLM 7B needs ~16.0 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~33 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) — 16.0 GB, 32.8 tok/s, Runs well

16.0 GB required23.0 GB available

70% VRAM used

Fit status

Runs well

Decode

32.8 tok/s

TTFT

5905 ms

Safe context

Memory

16.0 GB / 23.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 7B on MacBook Pro M2 Pro 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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	32.8 tok/s	3221 ms	4K
Coding	C	Runs well	32.8 tok/s	5905 ms	4K
Agentic Coding	C	Runs with offload (needs ~0 GB host RAM)	32.0 tok/s	8811 ms	4K
Reasoning	C	Runs well	32.8 tok/s	6978 ms	4K
RAG	C	Runs with offload (needs ~0 GB host RAM)	32.0 tok/s	11014 ms	4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C43
Q3_K_S	3	3.4 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run DeepSeek LLM 7B on your machine.

Run

ollama run deepseek-llm

Upgrade options

Hardware that runs DeepSeek LLM 7B well

Intel Arc Pro B60 24GBBest value

456 GB/s (+256)

Raises estimated decode speed by about 76%.57.7 tok/s decode

Raises estimated decode speed by about 76%.

~$599 MSRP

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+4)410 GB/s (+210)

Raises estimated decode speed by about 101%.65.9 tok/s decode

Raises estimated decode speed by about 101%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run DeepSeek LLM 7B?

Yes, MacBook Pro M2 Pro 32GB can run DeepSeek LLM 7B with a C grade (Runs well). Expected decode speed: 32.8 tok/s.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 7B?

The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 7B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, DeepSeek LLM 7B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on MacBook Pro M2 Pro 32GB receives a C grade with 32.8 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for DeepSeek LLM 7B?

See all results for MacBook Pro M2 Pro 32GB See all hardware for DeepSeek LLM 7B

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