cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~32.2 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 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
16.4 tok/s
TTFT
11810 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 | 16.4 tok/s | 6442 ms | 357K |
| Coding | C | Runs well | 16.4 tok/s | 11810 ms | 357K |
| Agentic Coding | C | Runs well | 16.4 tok/s | 17178 ms | 357K |
| Reasoning | C | Runs well | 16.4 tok/s | 13957 ms | 357K |
| RAG | C | Runs well | 16.4 tok/s | 21472 ms | 357K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M3 Max 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 |
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 startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 132%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$6,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 1949%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$8,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 16.4 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 MacBook Pro M3 Max 128GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11810ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M3 Max 128GB receives a C grade with 16.4 tok/s and 357K context.
On MacBook Pro M3 Max 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. MacBook Pro M3 Max 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.
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-m3-max-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: