cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~21.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 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
41.2 tok/s
TTFT
4700 ms
Safe context
74K
Memory
21.9 GB / 32.0 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 | 41.2 tok/s | 2564 ms | 74K |
| Coding | C | Runs well | 41.2 tok/s | 4700 ms | 74K |
| Agentic Coding | C | Runs well | 41.2 tok/s | 6837 ms | 74K |
| Reasoning | C | Runs well | 41.2 tok/s | 5555 ms | 74K |
| RAG | C | Runs well | 41.2 tok/s | 8546 ms | 74K |
How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C46 |
Q3_K_S | 3 | 11.8 GB | Low | C47 |
NVFP4 | 4 | 13.4 GB | Medium | C48 |
Q4_K_M | 4 | 14.6 GB | Medium | C48 |
Q5_K_M | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C49 |
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-bartowski--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server startUpgrade options
Yes, NVIDIA V100 32GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Runs well). Expected decode speed: 41.2 tok/s.
cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 21.9 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 NVIDIA V100 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 41.2 tokens per second decode speed with a time-to-first-token of 4700ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on NVIDIA V100 32GB receives a C grade with 41.2 tok/s and 74K context.
On NVIDIA V100 32GB, cognitivecomputations Dolphin Mistral 24B Venice Edition can safely use up to 74K 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-dolphin-mistral-24b-venice-edition-gguf-on-v100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
