RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.102.8 tok/s Dekodierung
Raises estimated decode speed by about 224%.
ca. $9,999 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on Mac Studio M2 Ultra 128GB?
YES — Runs Great
C46Usable
Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~32.2 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 32.2 GB, 31.7 tok/s, Runs well
32.2 GB required92.2 GB available
35% VRAM used
Fit status
Runs well
Decode
31.7 tok/s
TTFT
6108 ms
Safe context
357K
Memory
32.2 GB / 92.2 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on Mac Studio M2 Ultra 128GB
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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 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 | 31.7 tok/s | 3332 ms | 357K |
| Coding | C | Runs well | 31.7 tok/s | 6108 ms | 357K |
| Agentic Coding | C | Runs well | 31.7 tok/s | 8885 ms | 357K |
| Reasoning | C | Runs well | 31.7 tok/s | 7219 ms | 357K |
| RAG | C | Runs well | 31.7 tok/s | 11106 ms | 357K |
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D40 |
Q3_K_S | 3 | 11.8 GB | Low | D40 |
NVFP4 | 4 | 13.4 GB | Medium | D40 |
Q4_K_M | 4 | 14.6 GB | Medium | C40 |
Q5_K_M | 5 | 17.3 GB | High | C40 |
Q6_K | 6 | 19.7 GB | High | C41 |
Q8_0 | 8 | 25.7 GB | Very High | C42 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C47 |
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-Optionen
Hardware, die cognitivecomputations Dolphin3.0 R1 Mistral 24B gut ausführt
Frequently asked questions
Can Mac Studio M2 Ultra 128GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Yes, Mac Studio M2 Ultra 128GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 31.7 tok/s.
How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 32.2 GB of memory with Q4_K_M quantization.
What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6108ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 128GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Mac Studio M2 Ultra 128GB receives a C grade with 31.7 tok/s and 357K context.
What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on Mac Studio M2 Ultra 128GB?
On Mac Studio M2 Ultra 128GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 357K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Not always. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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