cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~46.0 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~38 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
38.0 tok/s
TTFT
5089 ms
Safe context
803K
Memory
46.0 GB / 184.3 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 | 38.0 tok/s | 2776 ms | 803K |
| Coding | C | Runs well | 38.0 tok/s | 5089 ms | 803K |
| Agentic Coding | C | Runs well | 38.0 tok/s | 7403 ms | 803K |
| Reasoning | C | Runs well | 38.0 tok/s | 6015 ms | 803K |
| RAG | C | Runs well | 38.0 tok/s | 9253 ms | 803K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D37 |
Q3_K_S | 3 | 11.8 GB | Low | D37 |
NVFP4 | 4 | 13.4 GB | Medium | D37 |
Q4_K_M | 4 | 14.6 GB | Medium | D37 |
Q5_K_M | 5 | 17.3 GB | High | D37 |
Q6_K | 6 | 19.7 GB | High | D38 |
Q8_0 | 8 | 25.7 GB | Very High | D38 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C41 |
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 784%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Raises estimated decode speed by about 643%.
~$15,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 38.0 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 46.0 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 M3 Ultra 256GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5089ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Mac Studio M3 Ultra 256GB receives a C grade with 38.0 tok/s and 803K context.
On Mac Studio M3 Ultra 256GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 803K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M3 Ultra 256GB 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-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: