Can Solar Open 69B REAP i1 run on MacBook Pro M4 Max 96GB?

YES — Tight Fit

C48Usable

○Estimated from fit model

Solar Open 69B REAP i1 needs ~61.4 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 61.4 GB, 14.3 tok/s, Tight fit

61.4 GB required69.1 GB available

89% VRAM used

Fit status

Tight fit

Decode

14.3 tok/s

TTFT

13568 ms

Safe context

31K

Memory

61.4 GB / 69.1 GB

Memory breakdown

Weights42.1 GB

KV Cache8.1 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsSolar Open 69B REAP i1 on MacBook Pro M4 Max 96GB
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: 14.3 tok/s decode · 13.6s TTFT (warm) · 36 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	Tight fit	14.3 tok/s	7401 ms	31K
Coding	C	Tight fit	14.3 tok/s	13568 ms	31K
Agentic Coding	C	Runs with offload (needs ~0.2 GB host RAM)	14.0 tok/s	20065 ms	31K
Reasoning	C	Tight fit	14.3 tok/s	16035 ms	31K
RAG	C	Runs with offload (needs ~0.2 GB host RAM)	14.0 tok/s	25081 ms	31K

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.9 GB	Low	C45
Q3_K_S	3	33.8 GB	Low	C47
NVFP4	4	38.6 GB	Medium	C48
Q4_K_M	4	42.1 GB	Medium	C48
Q5_K_M	5	49.7 GB	High	C48
Q6_KBest for your GPU	6	56.6 GB	High	C48
Q8_0	8	73.8 GB	Very High	F0
F16	16	141.5 GB	Maximum	F0