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URL: https://willitrunai.com/can-run/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-m2-max-96gb


Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

C45Usable
Estimated from fit model

cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~28.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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) — 28.7 GB, 15.8 tok/s, Runs well
28.7 GB required69.1 GB available
42% VRAM used

Fit status

Runs well

Decode

15.8 tok/s

TTFT

12217 ms

Safe context

246K

Memory

28.7 GB / 69.1 GB

Memory breakdown

Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom10.4 GB

See how fast it feels

See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M2 Max 96GB
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: 15.8 tok/s decode · 12.2s TTFT (warm) · 40 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well15.8 tok/s6664 ms246K
CodingCRuns well15.8 tok/s12217 ms246K
Agentic CodingCRuns well15.8 tok/s17770 ms246K
ReasoningCRuns well15.8 tok/s14438 ms246K
RAGCRuns well15.8 tok/s22212 ms246K

Quantization options

How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.4 GB
LowC41
Q3_K_S
3
11.8 GB
LowC41
NVFP4
4

Get started

Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.

Run

lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server start

Upgrade options

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

Mac Studio M2 Ultra 128GBBudget pick
128 GB Unified (+32)800 GB/s (+400)
C
Raises estimated decode speed by about 101%.31.7 tok/s decode

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value
128 GB Unified (+32)800 GB/s (+400)
C
Raises estimated decode speed by about 91%.30.1 tok/s decode

Raises estimated decode speed by about 91%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

MacBook Pro M4 Max 128GBApple upgrade
128 GB Unified (+32)546 GB/s (+146)
C
Raises estimated decode speed by about 116%.34.2 tok/s decode

Raises estimated decode speed by about 116%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Max 96GBSee all hardware for cognitivecomputations Dolphin3.0 R1 Mistral 24B
13.4 GB
Medium
C41
Q4_K_M
4
14.6 GB
MediumC42
Q5_K_M
5
17.3 GB
HighC42
Q6_K
6
19.7 GB
HighC43
Q8_0
8
25.7 GB
Very HighC44
F16Best for your GPU
16
49.2 GB
MaximumC48

On MacBook Pro M2 Max 96GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 246K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.