cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~26.7 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~115 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
114.8 tok/s
TTFT
1687 ms
Safe context
319K
Memory
26.7 GB / 80.0 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 | 114.8 tok/s | 920 ms | 319K |
| Coding | C | Runs well | 114.8 tok/s | 1687 ms | 319K |
| Agentic Coding | C | Runs well | 114.8 tok/s | 2454 ms | 319K |
| Reasoning | C | Runs well | 114.8 tok/s | 1994 ms | 319K |
| RAG | C | Runs well | 114.8 tok/s | 3067 ms | 319K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C40 |
Q3_K_S | 3 | 11.8 GB | Low | C40 |
NVFP4 | 4 | 13.4 GB | Medium | C41 |
Q4_K_M | 4 | 14.6 GB | Medium | C41 |
Q5_K_M | 5 | 17.3 GB | High | C41 |
Q6_K | 6 | 19.7 GB | High | C42 |
Q8_0 | 8 | 25.7 GB | Very High | C43 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C48 |
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 startYes, NVIDIA H100 PCIe 80GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 114.8 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 26.7 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 H100 PCIe 80GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 114.8 tokens per second decode speed with a time-to-first-token of 1687ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on NVIDIA H100 PCIe 80GB receives a C grade with 114.8 tok/s and 319K context.
On NVIDIA H100 PCIe 80GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 319K 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-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: