RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick1792 GB/s (+992)
C
Raises estimated decode speed by about 224%.102.8 tok/s decode
Raises estimated decode speed by about 224%.
~$9,999 MSRP
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.
Operating mode
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
Fit status
Runs well
Decode
31.7 tok/s
TTFT
6108 ms
Safe context
357K
Memory
32.2 GB / 92.2 GB
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.
| 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 |
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 |
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
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 224%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 189%.
~$9,999 MSRP
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.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 32.2 GB of memory with Q4_K_M quantization.
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.
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.