Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M4 Pro 64GB?

YES — Runs Great

C49Usable

▲Estimated — low-sample bucket· few comparable runs

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~25.3 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 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) — 25.3 GB, 21.5 tok/s, Runs well

25.3 GB required46.1 GB available

55% VRAM used

Fit status

Runs well

Decode

21.5 tok/s

TTFT

8989 ms

Safe context

134K

Memory

25.3 GB / 46.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M4 Pro 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 21.5 tok/s decode · 9.0s TTFT (warm) · 54 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	21.5 tok/s	4903 ms	134K
Coding	C	Runs well	21.5 tok/s	8989 ms	134K
Agentic Coding	C	Runs well	21.5 tok/s	13075 ms	134K
Reasoning	C	Runs well	21.5 tok/s	10623 ms	134K
RAG	C	Runs well	21.5 tok/s	16343 ms	134K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C43
Q3_K_S	3	11.8 GB	Low	C44
NVFP4	4	13.4 GB	Medium	C44
Q4_K_M	4	14.6 GB	Medium	C45
Q5_K_M	5	17.3 GB	High	C46
Q6_K	6	19.7 GB	High	C46
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0