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URL: https://willitrunai.com/can-run/hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf-on-m2-pro-32gb

⇱ logos16v2 stablelm2 1.6b i1 on MacBook Pro M2 Pro 32GB? YES

Can logos16v2 stablelm2 1.6b i1 run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

C42Usable
○Estimated from fit model

logos16v2 stablelm2 1.6b i1 needs ~5.5 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~22 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) — 5.5 GB, 22.4 tok/s, Runs well
5.5 GB required23.0 GB available
24% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

1.5M

Memory

5.5 GB / 23.0 GB

Memory breakdown

Weights1.0 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelslogos16v2 stablelm2 1.6b i1 on MacBook Pro M2 Pro 32GB
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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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 well22.4 tok/s4714 ms1.4M
CodingCRuns well22.4 tok/s8643 ms1.5M
Agentic CodingCRuns well22.4 tok/s12571 ms1.5M
ReasoningCRuns well22.4 tok/s10214 ms1.5M
RAGCRuns well22.4 tok/s15714 ms1.5M

Quantization options

How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowC43
Q3_K_S
3
0.8 GB
LowC43
NVFP4
4
0.9 GB
MediumC43
Q4_K_M
4
1.0 GB
MediumC43
Q5_K_M
5
1.2 GB
HighC44
Q6_K
6
1.3 GB
HighC44
Q8_0
8
1.7 GB
Very HighC44
F16Best for your GPU
16
3.3 GB
MaximumC44

Get started

Copy-paste commands to run logos16v2 stablelm2 1.6b i1 on your machine.

Run

lms load hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf && lms server start

Frequently asked questions

See all results for MacBook Pro M2 Pro 32GBSee all hardware for logos16v2 stablelm2 1.6b i1