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.
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
15.8 tok/s
TTFT
12217 ms
Safe context
246K
Memory
28.7 GB / 69.1 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 | 15.8 tok/s | 6664 ms | 246K |
| Coding | C | Runs well | 15.8 tok/s | 12217 ms | 246K |
| Agentic Coding | C | Runs well | 15.8 tok/s | 17770 ms | 246K |
| Reasoning | C | Runs well | 15.8 tok/s | 14438 ms | 246K |
| RAG | C | Runs well | 15.8 tok/s | 22212 ms | 246K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C41 |
Q3_K_S | 3 | 11.8 GB | Low | C41 |
NVFP4 | 4 | 13.4 GB | Medium | C41 |
Q4_K_M | 4 | 14.6 GB | Medium | C42 |
Q5_K_M | 5 | 17.3 GB | High | C42 |
Q6_K | 6 | 19.7 GB | High | C43 |
Q8_0 | 8 | 25.7 GB | Very High | C44 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C48 |
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 startOpções de upgrade
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 15.8 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 28.7 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 MacBook Pro M2 Max 96GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12217ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M2 Max 96GB receives a C grade with 15.8 tok/s and 246K context.
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.
Not always. MacBook Pro M2 Max 96GB 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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<iframe src="https://willitrunai.com/embed/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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