Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M1 Pro 32GB?

YES — Tight Fit

C46Usable

○Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.8 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 21.8 GB, 8.9 tok/s, Tight fit

21.8 GB required23.0 GB available

95% VRAM used

Fit status

Tight fit

Decode

8.9 tok/s

TTFT

21802 ms

Safe context

23K

Memory

21.8 GB / 23.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M1 Pro 32GB
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: 8.9 tok/s decode · 21.8s TTFT (warm) · 22 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	8.9 tok/s	11892 ms	23K
Coding	C	Tight fit	8.9 tok/s	21802 ms	23K
Agentic Coding	D	Runs with offload (needs ~0.9 GB host RAM)	7.9 tok/s	35680 ms	23K
Reasoning	C	Tight fit	8.9 tok/s	25766 ms	23K
RAG	D	Runs with offload (needs ~0.9 GB host RAM)	7.9 tok/s	44600 ms	23K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C49
Q3_K_S	3	11.8 GB	Low	C50
NVFP4	4	13.4 GB	Medium	C50
Q4_K_M	4	14.6 GB	Medium	C50
Q5_K_MBest for your GPU	5	17.3 GB	High	C50
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0