Can SmolLM3 3B run on MacBook Pro M3 24GB?

YES — Runs Great

B56Good

Estimated from fit model

SmolLM3 3B needs ~7.3 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~40 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) — 7.3 GB, 39.9 tok/s, Runs well

7.3 GB required17.3 GB available

42% VRAM used

Fit status

Runs well

Decode

39.9 tok/s

TTFT

4847 ms

Safe context

98K

Memory

7.3 GB / 17.3 GB

Memory breakdown

Weights1.8 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsSmolLM3 3B on MacBook Pro M3 24GB
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: 39.9 tok/s decode · 4.8s TTFT (warm) · 100 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	39.9 tok/s	2644 ms	98K
Coding	B	Runs well	39.9 tok/s	4847 ms	98K
Agentic Coding	B	Runs well	39.9 tok/s	7050 ms	98K
Reasoning	B	Runs well	39.9 tok/s	5728 ms	98K
RAG	B	Runs well	39.9 tok/s	8812 ms	98K

Quantization options

How SmolLM3 3B (3B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C53
Q3_K_S	3	1.5 GB	Low	C53
NVFP4	4	1.7 GB	Medium	C53
Q4_K_M	4	1.8 GB	Medium	C53
Q5_K_M	5	2.2 GB	High	C54
Q6_K	6	2.5 GB	High	C54
Q8_0	8	3.2 GB	Very High	C54
F16Best for your GPU	16	6.1 GB	Maximum	B57

Get started

Copy-paste commands to run SmolLM3 3B on your machine.

Run

lms load SmolLM3-3B && lms server start

Frequently asked questions

Can MacBook Pro M3 24GB run SmolLM3 3B?

Yes, MacBook Pro M3 24GB can run SmolLM3 3B with a B grade (Runs well). Expected decode speed: 39.9 tok/s.

How much VRAM does SmolLM3 3B need?

SmolLM3 3B (3B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.

What is the best quantization for SmolLM3 3B?

The recommended quantization for SmolLM3 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will SmolLM3 3B run at on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, SmolLM3 3B achieves approximately 39.9 tokens per second decode speed with a time-to-first-token of 4847ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run SmolLM3 3B for coding?

For coding workloads, SmolLM3 3B on MacBook Pro M3 24GB receives a B grade with 39.9 tok/s and 98K context.

What context window can SmolLM3 3B use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, SmolLM3 3B can safely use up to 98K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 24GB as fast as VRAM for SmolLM3 3B?

Not always. MacBook Pro 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.

See all results for MacBook Pro M3 24GB See all hardware for SmolLM3 3B

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