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URL: https://willitrunai.com/can-run/hf-bingsu--exaone-3-0-7-8b-it-on-m2-ultra-128gb

⇱ exaone 3.0 7.8b it on Mac Studio M2 Ultra 128GB? YES

Can exaone 3.0 7.8b it run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C46Usable
○Estimated from fit model

exaone 3.0 7.8b it needs ~20.4 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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) — 20.4 GB, 97.5 tok/s, Runs well
20.4 GB required92.2 GB available
22% VRAM used

Fit status

Runs well

Decode

97.5 tok/s

TTFT

1985 ms

Safe context

1.3M

Memory

20.4 GB / 92.2 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom13.8 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it on Mac Studio M2 Ultra 128GB
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: 97.5 tok/s decode · 2.0s TTFT (warm) · 244 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 well97.5 tok/s1083 ms1.3M
CodingCRuns well97.5 tok/s1985 ms1.3M
Agentic CodingCRuns well97.5 tok/s2888 ms1.3M
ReasoningCRuns well97.5 tok/s2346 ms1.3M
RAGCRuns well97.5 tok/s3610 ms1.3M

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowD39
Q3_K_S
3
3.8 GB
LowD39
NVFP4
4
4.4 GB
MediumD39
Q4_K_M
4
4.8 GB
MediumD39
Q5_K_M
5
5.6 GB
HighD39
Q6_K
6
6.4 GB
HighD39
Q8_0
8
8.3 GB
Very HighD39
F16Best for your GPU
16
16.0 GB
MaximumD40

Get started

Copy-paste commands to run exaone 3.0 7.8b it on your machine.

Run

lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GBSee all hardware for exaone 3.0 7.8b it