Can DeepSeek LLM 7B run on MacBook Air M3 24GB?

Q: Is unified memory on MacBook Air M3 24GB as fast as VRAM for DeepSeek LLM 7B?

Not always. MacBook Air M3 24GB 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 — Tight Fit

C47Usable

Estimated from fit model

DeepSeek LLM 7B needs ~15.1 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.1 GB, 15.9 tok/s, Tight fit

15.1 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

15.9 tok/s

TTFT

12157 ms

Safe context

Memory

15.1 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 7B on MacBook Air M3 24GB
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: 15.9 tok/s decode · 12.2s TTFT (warm) · 40 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	15.9 tok/s	6631 ms	4K
Coding	C	Tight fit	15.9 tok/s	12157 ms	4K
Agentic Coding	F	Too heavy	11.0 tok/s	25533 ms	4K
Reasoning	C	Tight fit	15.9 tok/s	14367 ms	4K
RAG	F	Too heavy	11.0 tok/s	31916 ms	4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C45
Q3_K_S	3	3.4 GB	Low	C45
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

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode

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

~$799 MSRP

RX 7900 XT 20GBBiggest leap

800 GB/s (+700)

Raises estimated decode speed by about 516%.98 tok/s decode

Raises estimated decode speed by about 516%.

~$899 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode

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

~$1,099 MSRP

MacBook Pro M4 Pro 64GBApple upgrade

64 GB Unified (+40)273 GB/s (+173)

Raises estimated decode speed by about 185%.45.3 tok/s decode

Raises estimated decode speed by about 185%.

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

~$1,599 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run DeepSeek LLM 7B?

Yes, MacBook Air M3 24GB can run DeepSeek LLM 7B with a C grade (Tight fit). Expected decode speed: 15.9 tok/s.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 15.1 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 Air M3 24GB?

On MacBook Air M3 24GB, DeepSeek LLM 7B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12157ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on MacBook Air M3 24GB receives a C grade with 15.9 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, 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 Air M3 24GB as fast as VRAM for DeepSeek LLM 7B?

See all results for MacBook Air M3 24GB See all hardware for DeepSeek LLM 7B

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