Can exaone 3.0 7.8b it run on MacBook Pro M3 Max 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

exaone 3.0 7.8b it needs ~13.5 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

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, 50.4 tok/s, Runs well

13.5 GB required46.1 GB available

29% VRAM used

Fit status

Runs well

Decode

50.4 tok/s

TTFT

3838 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 MacBook Pro M3 Max 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: 50.4 tok/s decode · 3.8s TTFT (warm) · 126 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	50.4 tok/s	2094 ms	587K
Coding	C	Runs well	50.4 tok/s	3838 ms	587K
Agentic Coding	C	Runs well	50.4 tok/s	5583 ms	587K
Reasoning	C	Runs well	50.4 tok/s	4536 ms	587K
RAG	C	Runs well	50.4 tok/s	6978 ms	587K

Inference speed

exaone 3.0 7.8b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for exaone 3.0 7.8b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	109.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	109.2	Fits
RTX 3090 24GB	24 GB	Q4_K_M	109.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	109.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	97.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	92.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	79.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	78.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	78.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	50.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	49.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	46.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	41.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C41
Q3_K_S	3	3.8 GB	Low	C41
NVFP4	4	4.4 GB	Medium	C41
Q4_K_M	4	4.8 GB	Medium	C42
Q5_K_M	5	5.6 GB	High	C42
Q6_K	6	6.4 GB	High	C42
Q8_0	8	8.3 GB	Very High	C42
F16Best for your GPU	16	16.0 GB	Maximum	C45

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

Opciones de mejora

Hardware que ejecuta bien exaone 3.0 7.8b it

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+32)546 GB/s (+146)

Sube la velocidad estimada de decodificación alrededor de un 56%.78.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 56%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejor relación calidad-precio

96 GB Unified (+32)819 GB/s (+419)

Sube la velocidad estimada de decodificación alrededor de un 117%.109.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 117%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Mac Studio M2 Ultra 128GBMejora Apple

128 GB Unified (+64)800 GB/s (+400)

Sube la velocidad estimada de decodificación alrededor de un 93%.97.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 93%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 64GB run exaone 3.0 7.8b it?

Yes, MacBook Pro M3 Max 64GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 50.4 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.

What is the best quantization for exaone 3.0 7.8b it?

The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.

What speed will exaone 3.0 7.8b it run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, exaone 3.0 7.8b it achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3838ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on MacBook Pro M3 Max 64GB receives a C grade with 50.4 tok/s and 587K context.

What context window can exaone 3.0 7.8b it use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, exaone 3.0 7.8b it can safely use up to 587K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for exaone 3.0 7.8b it?

Not always. MacBook Pro M3 Max 64GB 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 Max 64GB See all hardware for exaone 3.0 7.8b it

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