RTX 3090 24GBOpção econômica24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 145%.34.1 tok/s decodificação
Raises estimated decode speed by about 145%.
~$1,499 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on RTX 4000 Ada 20GB?
YES — With Offload
C48Usable
Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~20.4 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~14 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 20.4 GB, 13.9 tok/s, Runs with offload (needs ~0.3 GB host RAM)
20.4 GB required20.0 GB available
102% VRAM needed
0.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
13.9 tok/s
TTFT
13962 ms
Safe context
14K
Memory
20.4 GB / 20.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX 4000 Ada 20GB
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: 13.9 tok/s decode · 14.0s TTFT (warm) · 35 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 19.2 tok/s | 5506 ms | 14K |
| Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 13.9 tok/s | 13962 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~2 GB host RAM) | 10.6 tok/s | 26670 ms | 14K |
| Reasoning | C | Runs with offload (needs ~0.3 GB host RAM) | 13.9 tok/s | 16501 ms | 14K |
| RAG | D | Very compromised (needs ~2 GB host RAM) | 10.6 tok/s | 33337 ms | 14K |
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C51 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 14.6 GB | Medium | C50 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
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 startOpções de upgrade
Hardware que roda bem cognitivecomputations Dolphin3.0 R1 Mistral 24B
RTX 3090 24GBOpção econômicaRTX PRO 4000 Blackwell 24GBMelhor custo-benefício24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 178%.38.6 tok/s decodificação
Raises estimated decode speed by about 178%.
~$1,599 MSRP
RTX 4090 24GBUpgrade NVIDIA24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 168%.37.2 tok/s decodificação
Raises estimated decode speed by about 168%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Yes, RTX 4000 Ada 20GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 13.9 tok/s.
How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 20.4 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 13.9 tokens per second decode speed with a time-to-first-token of 13962ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on RTX 4000 Ada 20GB receives a C grade with 13.9 tok/s and 14K context.
What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if cognitivecomputations Dolphin3.0 R1 Mistral 24B feels slow on RTX 4000 Ada 20GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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