Mac mini M4 64GBBudget pick
64 GB Unified (+28)
C
Adds memory headroom for longer context windows and future model growth.8.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M3 Pro 36GB?
YES — Tight Fit
C46Usable
○Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~22.2 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~8 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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.
Select quantization to explore
Q4_K_M (Medium quality) — 22.2 GB, 7.5 tok/s, Tight fit
22.2 GB required25.9 GB available
86% VRAM used
Fit status
Tight fit
Decode
7.5 tok/s
TTFT
25884 ms
Safe context
37K
Memory
22.2 GB / 25.9 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M3 Pro 36GB
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: 7.5 tok/s decode · 25.9s TTFT (warm) · 19 tok/s prefill
What limits this setup
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.5 tok/s | 14119 ms | 37K |
| Coding | C | Tight fit | 7.5 tok/s | 25884 ms | 37K |
| Agentic Coding | C | Runs with offload | 7.5 tok/s | 37650 ms | 37K |
| Reasoning | C | Tight fit | 7.5 tok/s | 30590 ms | 37K |
| RAG | C | Runs with offload | 7.5 tok/s | 47062 ms | 37K |
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C48 |
Q3_K_S | 3 | 11.8 GB | Low | C49 |
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 startUpgrade options
Hardware that runs cognitivecomputations Dolphin3.0 R1 Mistral 24B well
MacBook Pro M4 Pro 64GBBest value
64 GB Unified (+28)273 GB/s (+123)
C
Raises estimated decode speed by about 187%.21.5 tok/s decode
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
MacBook Pro M4 Max 48GBApple upgrade
48 GB Unified (+12)546 GB/s (+396)
C
Raises estimated decode speed by about 356%.34.2 tok/s decode
Raises estimated decode speed by about 356%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP