Can OLMo 2 7B run on MacBook Air M4 24GB?

YES — Runs Great

B70Good

Estimated — low-sample bucket· few comparable runs

OLMo 2 7B needs ~9.7 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~20 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) — 9.7 GB, 20.0 tok/s, Runs well

9.7 GB required17.3 GB available

56% VRAM used

Fit status

Runs well

Decode

20.0 tok/s

TTFT

9674 ms

Safe context

Memory

9.7 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsOLMo 2 7B on MacBook Air M4 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: 20.0 tok/s decode · 9.7s TTFT (warm) · 50 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	B	Runs well	20.0 tok/s	5277 ms	4K
Coding	B	Runs well	20.0 tok/s	9674 ms	4K
Agentic Coding	A	Runs well	20.0 tok/s	14072 ms	4K
Reasoning	B	Runs well	20.0 tok/s	11433 ms	4K
RAG	A	Runs well	20.0 tok/s	17590 ms	4K

Quantization options

How OLMo 2 7B (7B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B67
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4

Get started

Copy-paste commands to run OLMo 2 7B on your machine.

Run

ollama run olmo2:7b

Upgrade options

Hardware that runs OLMo 2 7B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+280)

Raises estimated decode speed by about 192%.58.4 tok/s decode

Raises estimated decode speed by about 192%.

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

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)410 GB/s (+290)

Raises estimated decode speed by about 255%.70.9 tok/s decode

Raises estimated decode speed by about 255%.

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

~$2,499 MSRP

MacBook Pro M1 Max 32GBApple upgrade

32 GB Unified (+8)400 GB/s (+280)

Raises estimated decode speed by about 177%.55.4 tok/s decode

Raises estimated decode speed by about 177%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Air M4 24GB run OLMo 2 7B?

Yes, MacBook Air M4 24GB can run OLMo 2 7B with a B grade (Runs well). Expected decode speed: 20.0 tok/s.

How much VRAM does OLMo 2 7B need?

OLMo 2 7B (7B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 7B?

The recommended quantization for OLMo 2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 7B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, OLMo 2 7B achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9674ms using Q4_K_M quantization.

Can MacBook Air M4 24GB run OLMo 2 7B for coding?

For coding workloads, OLMo 2 7B on MacBook Air M4 24GB receives a B grade with 20.0 tok/s and 4K context.

What context window can OLMo 2 7B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, OLMo 2 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 M4 24GB as fast as VRAM for OLMo 2 7B?

Not always. MacBook Air M4 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.

See all results for MacBook Air M4 24GB See all hardware for OLMo 2 7B

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