cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~19.9 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.
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
7.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.6 tok/s
TTFT
34578 ms
Safe context
4K
Memory
19.9 GB / 12.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 19.9 GB, but this setup only exposes 12.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.5 tok/s | 16158 ms | 4K |
| Coding | F | Too heavy | 5.6 tok/s | 34578 ms | 4K |
| Agentic Coding | F | Too heavy | 4.2 tok/s | 66474 ms | 4K |
| Reasoning | F | Too heavy | 5.6 tok/s | 40865 ms | 4K |
| RAG | F | Too heavy | 4.2 tok/s | 83093 ms | 4K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 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 |
Opciones de mejora
RTX 4000 Ada 20GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,250 MSRP
RTX 3090 24GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
No, cognitivecomputations Dolphin3.0 R1 Mistral 24B requires more memory than RTX 4000 Ada Laptop 12GB provides.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 19.9 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 RTX 4000 Ada Laptop 12GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 5.6 tokens per second decode speed with a time-to-first-token of 34578ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX 4000 Ada Laptop 12GB receives a F grade with 5.6 tok/s and 4K context.
On RTX 4000 Ada Laptop 12GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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-rtx-4000-ada-laptop-12gb" 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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