Can internlm2 limarp chat 20b run on MacBook Air M4 24GB?

YES — With Offload

C46Usable

▲Estimated — low-sample bucket· few comparable runs

internlm2 limarp chat 20b needs ~18.0 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 18.0 GB, 8.5 tok/s, Runs with offload (needs ~0.5 GB host RAM)

18.0 GB required17.3 GB available

104% VRAM needed

0.7 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.5 GB host RAM)

Decode

8.5 tok/s

TTFT

22833 ms

Safe context

11K

Memory

18.0 GB / 17.3 GB

Memory breakdown

Weights12.2 GB

KV Cache2.3 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsinternlm2 limarp chat 20b 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: 8.5 tok/s decode · 22.8s TTFT (warm) · 21 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	9.2 tok/s	11470 ms	11K
Coding	C	Runs with offload (needs ~0.5 GB host RAM)	8.5 tok/s	22833 ms	11K
Agentic Coding	D	Very compromised (needs ~1.9 GB host RAM)	7.2 tok/s	39297 ms	11K
Reasoning	C	Runs with offload (needs ~0.5 GB host RAM)	8.5 tok/s	26984 ms	11K
RAG	D	Very compromised (needs ~1.9 GB host RAM)	7.2 tok/s	49122 ms	11K

Quantization options

How internlm2 limarp chat 20b (20B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4	11.2 GB	Medium	C50
Q4_K_MBest for your GPU	4	12.2 GB	Medium	C50
Q5_K_M	5	14.4 GB	High	F0
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0