RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1120)
B
Raises estimated decode speed by about 159%.82 tok/sデコード
Raises estimated decode speed by about 159%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on Quadro RTX 6000 24GB?
YES — Tight Fit
C50Usable
Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.1 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 21.1 GB, 31.7 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
31.7 tok/s
TTFT
6113 ms
Safe context
33K
Memory
21.1 GB / 24.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on Quadro RTX 6000 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 31.7 tok/s | 3334 ms | 33K |
| Coding | C | Tight fit | 31.7 tok/s | 6113 ms | 33K |
| Agentic Coding | C | Runs with offload | 31.7 tok/s | 8891 ms | 33K |
| Reasoning | C | Tight fit | 31.7 tok/s | 7224 ms | 33K |
| RAG | C | Runs with offload | 31.7 tok/s | 11114 ms | 33K |
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Quadro RTX 6000 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 |
Get started
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
cognitivecomputations Dolphin3.0 R1 Mistral 24Bを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+224)
C
Raises estimated decode speed by about 62%.51.4 tok/sデコード
Raises estimated decode speed by about 62%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can Quadro RTX 6000 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Yes, Quadro RTX 6000 24GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Tight fit). Expected decode speed: 31.7 tok/s.
How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 21.1 GB of memory with Q4_K_M quantization.
What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on Quadro RTX 6000 24GB?
On Quadro RTX 6000 24GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6113ms using Q4_K_M quantization.
Can Quadro RTX 6000 24GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Quadro RTX 6000 24GB receives a C grade with 31.7 tok/s and 33K context.
What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on Quadro RTX 6000 24GB?
On Quadro RTX 6000 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.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf-on-quadro-rtx-6000-24gb" 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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