cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.1 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~32 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
32.0 tok/s
TTFT
6056 ms
Safe context
33K
Memory
21.1 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 32.0 tok/s | 3303 ms | 33K |
| Coding | C | Tight fit | 32.0 tok/s | 6056 ms | 33K |
| Agentic Coding | C | Runs with offload | 32.0 tok/s | 8809 ms | 33K |
| Reasoning | C | Tight fit | 32.0 tok/s | 7157 ms | 33K |
| RAG | C | Runs with offload | 32.0 tok/s | 11011 ms | 33K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA A10 24GB (24.0 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 | C50 |
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 startOpciones de mejora
RTX 5090 32GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 156%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 61%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
RTX 5000 Ada 32GBMejora NVIDIAAñade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$4,000 MSRP
Yes, NVIDIA A10 24GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Tight fit). Expected decode speed: 32.0 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 21.1 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 NVIDIA A10 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA A10 24GB receives a C grade with 32.0 tok/s and 33K context.
On NVIDIA A10 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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-a10-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: