cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~21.8 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~10 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
Tight fit
Decode
9.6 tok/s
TTFT
20245 ms
Safe context
23K
Memory
21.8 GB / 23.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 9.6 tok/s | 11043 ms | 23K |
| Coding | C | Tight fit | 9.6 tok/s | 20245 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 8.5 tok/s | 33132 ms | 23K |
| Reasoning | C | Tight fit | 9.6 tok/s | 23926 ms | 23K |
| RAG | D | Runs with offload | 8.5 tok/s | 41414 ms | 23K |
How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | C49 |
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 Dolphin Mistral 24B Venice Edition on your machine.
Run
lms load hf-yixman--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 124%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,599 MSRP
Sube la velocidad estimada de decodificación alrededor de un 256%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 71%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Yes, MacBook Pro M2 Pro 32GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Tight fit). Expected decode speed: 9.6 tok/s.
cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 21.8 GB of memory with Q4_K_M quantization.
The recommended quantization for cognitivecomputations Dolphin Mistral 24B Venice Edition is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20245ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on MacBook Pro M2 Pro 32GB receives a C grade with 9.6 tok/s and 23K context.
On MacBook Pro M2 Pro 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 23K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. MacBook Pro M2 Pro 32GB 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-yixman--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf-on-m2-pro-32gb" 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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