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URL: https://willitrunai.com/can-run/hf-bingsu--exaone-3-0-7-8b-it-on-m1-ultra-64gb

⇱ exaone 3.0 7.8b it on Mac Studio M1 Ultra 64GB? YES

Can exaone 3.0 7.8b it run on Mac Studio M1 Ultra 64GB?

YES — Runs Great

C48Usable
○Estimated from fit model

exaone 3.0 7.8b it needs ~13.5 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~93 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) — 13.5 GB, 92.5 tok/s, Runs well
13.5 GB required46.1 GB available
29% VRAM used

Fit status

Runs well

Decode

92.5 tok/s

TTFT

2094 ms

Safe context

587K

Memory

13.5 GB / 46.1 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it on Mac Studio M1 Ultra 64GB
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: 92.5 tok/s decode · 2.1s TTFT (warm) · 231 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 well92.5 tok/s1142 ms587K
CodingCRuns well92.5 tok/s2094 ms587K
Agentic CodingCRuns well92.5 tok/s3045 ms587K
ReasoningCRuns well92.5 tok/s2474 ms587K
RAGCRuns well92.5 tok/s3806 ms587K

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC41
Q3_K_S
3
3.8 GB
LowC41
NVFP4
4
4.4 GB
MediumC41
Q4_K_M
4
4.8 GB
MediumC42
Q5_K_M
5
5.6 GB
HighC42
Q6_K
6
6.4 GB
HighC42
Q8_0
8
8.3 GB
Very HighC42
F16Best for your GPU
16
16.0 GB
MaximumC45

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 M1 Ultra 64GBSee all hardware for exaone 3.0 7.8b it