cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~15.0 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q2_K quantization, expect ~8 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
7.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.2 tok/s
TTFT
46475 ms
Safe context
4K
Memory
20.3 GB / 13.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.5 tok/s | 23367 ms | 4K |
| Coding | F | Too heavy | 4.2 tok/s | 46475 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 78069 ms | 4K |
| Reasoning | F | Too heavy | 4.2 tok/s | 54925 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 97587 ms | 4K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 | 13.4 GB | Medium | F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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 start升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$799 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
RTX 5090 32GB最大飞跃Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
Yes, MacBook Pro M3 Pro 18GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B at Q2_K quantization (Very compromised (needs ~1.3 GB host RAM)). The recommended Q4_K_M requires 20.3 GB which exceeds available memory, but at Q2_K it needs only 15.0 GB. Expected decode speed: 7.9 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 20.3 GB at Q4_K_M quantization. On MacBook Pro M3 Pro 18GB, it fits at Q2_K using 15.0 GB.
The recommended quantization is Q4_K_M, but on MacBook Pro M3 Pro 18GB the best fitting quantization is Q2_K, which uses 15.0 GB.
On MacBook Pro M3 Pro 18GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 7.9 tokens per second decode speed with a time-to-first-token of 24453ms using Q2_K quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M3 Pro 18GB receives a F grade with 4.2 tok/s and 4K context.
On MacBook Pro M3 Pro 18GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M3 Pro 18GB 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-m3-pro-18gb" 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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