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URL: https://willitrunai.com/can-run/hf-afrideva--stablelm-3b-4e1t-gguf-on-m2-24gb

⇱ stablelm 3b 4e1t on Mac mini M2 24GB? YES

Can stablelm 3b 4e1t run on Mac mini M2 24GB?

YES — Runs Great

C46Usable
○Estimated from fit model

stablelm 3b 4e1t needs ~5.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:

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) — 5.7 GB, 35.5 tok/s, Runs well
5.7 GB required17.3 GB available
33% VRAM used

Fit status

Runs well

Decode

35.5 tok/s

TTFT

5451 ms

Safe context

544K

Memory

5.7 GB / 17.3 GB

Memory breakdown

Weights1.8 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsstablelm 3b 4e1t on Mac mini M2 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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well35.5 tok/s2973 ms544K
CodingCRuns well35.5 tok/s5451 ms544K
Agentic CodingCRuns well35.5 tok/s7928 ms544K
ReasoningCRuns well35.5 tok/s6442 ms544K
RAGCRuns well35.5 tok/s9910 ms544K

Quantization options

How stablelm 3b 4e1t (3B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.2 GB
LowC45
Q3_K_S
3
1.5 GB
LowC45
NVFP4
4
1.7 GB
MediumC45
Q4_K_M
4
1.8 GB
MediumC45
Q5_K_M
5
2.2 GB
HighC45
Q6_K
6
2.5 GB
HighC46
Q8_0
8
3.2 GB
Very HighC46
F16Best for your GPU
16
6.1 GB
MaximumC49

Get started

Copy-paste commands to run stablelm 3b 4e1t on your machine.

Run

lms load hf-afrideva--stablelm-3b-4e1t-gguf && lms server start

Frequently asked questions

See all results for Mac mini M2 24GBSee all hardware for stablelm 3b 4e1t