RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+784)
B
Raises estimated decode speed by about 57%.82 tok/sデコード
Raises estimated decode speed by about 57%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on RTX 4090 24GB?
YES — Tight Fit
C52Usable
Estimated from fit model
cognitivecomputations Dolphin Mistral 24B Venice Edition needs ~21.1 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: 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, 52.3 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
52.3 tok/s
TTFT
3700 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 Dolphin Mistral 24B Venice Edition on RTX 4090 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 52.3 tok/s decode · 3.7s TTFT (warm) · 131 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 | B | Runs well | 52.3 tok/s | 2018 ms | 33K |
| Coding | C | Tight fit | 52.3 tok/s | 3700 ms | 33K |
| Agentic Coding | C | Runs with offload | 52.3 tok/s | 5381 ms | 33K |
| Reasoning | C | Tight fit | 52.3 tok/s | 4372 ms | 33K |
| RAG | C | Runs with offload | 52.3 tok/s | 6727 ms | 33K |
Quantization options
How cognitivecomputations Dolphin Mistral 24B Venice Edition (24B params) fits at each quantization level on RTX 4090 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 Dolphin Mistral 24B Venice Edition on your machine.
Run
lms load hf-bartowski--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server startアップグレードオプション
cognitivecomputations Dolphin Mistral 24B Venice Edition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)
C
Adds memory headroom for longer context windows and future model growth.51.4 tok/sデコード
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 RTX 4090 24GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?
Yes, RTX 4090 24GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition with a C grade (Tight fit). Expected decode speed: 52.3 tok/s.
How much VRAM does cognitivecomputations Dolphin Mistral 24B Venice Edition need?
cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 21.1 GB of memory with Q4_K_M quantization.
What is the best quantization for cognitivecomputations Dolphin Mistral 24B Venice Edition?
The recommended quantization for cognitivecomputations Dolphin Mistral 24B Venice Edition is Q4_K_M, which balances quality and memory efficiency.
What speed will cognitivecomputations Dolphin Mistral 24B Venice Edition run at on RTX 4090 24GB?
On RTX 4090 24GB, cognitivecomputations Dolphin Mistral 24B Venice Edition achieves approximately 52.3 tokens per second decode speed with a time-to-first-token of 3700ms using Q4_K_M quantization.
Can RTX 4090 24GB run cognitivecomputations Dolphin Mistral 24B Venice Edition for coding?
For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on RTX 4090 24GB receives a C grade with 52.3 tok/s and 33K context.
What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on RTX 4090 24GB?
On RTX 4090 24GB, cognitivecomputations Dolphin Mistral 24B Venice Edition 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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