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URL: https://willitrunai.com/can-run/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-m4-16gb

⇱ cognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook …

Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M4 16GB?

NO — Won't Fit

F0Won't run
▲Estimated — low-sample bucket· few comparable runs

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~20.1 GB but MacBook Pro M4 16GB only has 11.5 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity
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) — 20.1 GB, exceeds 11.5 GB available
20.1 GB required11.5 GB available
175% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.4 tok/s

TTFT

44232 ms

Safe context

4K

Memory

20.1 GB / 11.5 GB

Offload

40%

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M4 16GB
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: 4.4 tok/s decode · 44.2s TTFT (warm) · 11 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 20.1 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy4.7 tok/s22262 ms4K
CodingFToo heavy4.4 tok/s44232 ms4K
Agentic CodingFToo heavy4.0 tok/s70690 ms4K
ReasoningFToo heavy4.4 tok/s52274 ms4K
RAGFToo heavy4.0 tok/s88362 ms4K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowF0
Q3_K_S
3
11.8 GB
LowF0
NVFP4
4
13.4 GB
MediumF0
Q4_K_M
4
14.6 GB
MediumF0
Q5_K_M
5
17.3 GB
HighF0
Q6_K
6
19.7 GB
HighF0
Q8_0
8
25.7 GB
Very HighF0
F16
16
49.2 GB
MaximumF0

Upgrade options

Hardware that runs cognitivecomputations Dolphin3.0 R1 Mistral 24B well

MacBook Pro M4 32GBBudget pick
32 GB Unified (+16)
C
Makes the model fit on the accelerator instead of staying completely out of reach.8.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$799 MSRP

Mac mini M4 32GBBest value
32 GB Unified (+16)
C
Makes the model fit on the accelerator instead of staying completely out of reach.8.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 64GBApple upgrade
64 GB Unified (+48)
C
Makes the model fit on the accelerator instead of staying completely out of reach.8.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+16)1792 GB/s (+1672)
B
Makes the model fit on the accelerator instead of staying completely out of reach.65.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

See all results for MacBook Pro M4 16GBSee all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B