32 GB VRAM (+8)1792 GB/s (+1360)
B
Raises estimated decode speed by about 252%.82 tok/s decode
Raises estimated decode speed by about 252%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on RTX 4500 Ada 24GB?
YES — Tight Fit
C49Usable
○Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~21.1 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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, 23.3 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
23.3 tok/s
TTFT
8305 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 RTX 4500 Ada 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: 23.3 tok/s decode · 8.3s TTFT (warm) · 58 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 23.3 tok/s | 4530 ms | 33K |
| Coding | C | Tight fit | 23.3 tok/s | 8305 ms | 33K |
| Agentic Coding | C | Runs with offload | 23.3 tok/s | 12080 ms | 33K |
| Reasoning | C | Tight fit | 23.3 tok/s | 9815 ms | 33K |
| RAG | C | Runs with offload | 23.3 tok/s | 15100 ms | 33K |
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RTX 4500 Ada 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 |
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 startUpgrade options
Hardware that runs cognitivecomputations Dolphin3.0 R1 Mistral 24B well
32 GB VRAM (+8)896 GB/s (+464)
C
Raises estimated decode speed by about 121%.51.4 tok/s decode
Raises estimated decode speed by about 121%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+144)
C
Raises estimated decode speed by about 35%.31.5 tok/s decode
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP