cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~25.1 GB VRAM. NVIDIA A16 64GB has 64.0 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
Runs well
Decode
32.0 tok/s
TTFT
6056 ms
Safe context
238K
Memory
25.1 GB / 64.0 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 | 238K |
| Coding | C | Runs well | 32.0 tok/s | 6056 ms | 238K |
| Agentic Coding | C | Runs well | 32.0 tok/s | 8809 ms | 238K |
| Reasoning | C | Runs well | 32.0 tok/s | 7157 ms | 238K |
| RAG | C | Runs well | 32.0 tok/s | 11011 ms | 238K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C41 |
Q3_K_S | 3 | 11.8 GB | Low | C42 |
NVFP4 | 4 | 13.4 GB | Medium | C42 |
Q4_K_M | 4 | 14.6 GB | Medium | C42 |
Q5_K_M | 5 | 17.3 GB | High | C43 |
Q6_K | 6 | 19.7 GB | High | C43 |
Q8_0 | 8 | 25.7 GB | Very High | C45 |
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 startアップグレードオプション
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視Raises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレードRaises estimated decode speed by about 592%.
Adds memory headroom for longer context windows and future model growth.
〜$12,000 MSRP
Yes, NVIDIA A16 64GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 32.0 tok/s.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 25.1 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 A16 64GB, 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 A16 64GB receives a C grade with 32.0 tok/s and 238K context.
On NVIDIA A16 64GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 238K 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.
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