RTX 3090 24GBGünstige Wahl24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 145%.34.1 tok/s Dekodierung
Raises estimated decode speed by about 145%.
ca. $1,499 MSRP
Can cognitivecomputations Dolphin Mistral 24B Venice Edition run on RTX 4000 Ada 20GB?
YES — With Offload
C47Usable
Estimated from fit model
cognitivecomputations Dolphin Mistral 24B Venice Edition 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 Dolphin Mistral 24B Venice Edition on RTX 4000 Ada 20GB
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: 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 |
Inference speed
cognitivecomputations Dolphin Mistral 24B Venice Edition inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for cognitivecomputations Dolphin Mistral 24B Venice Edition at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 82.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 52.3 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 47.2 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 44.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 38.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 31.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 30.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 19.1 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 16.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 6.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 4.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How cognitivecomputations Dolphin Mistral 24B Venice Edition (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 | C50 |
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 Dolphin Mistral 24B Venice Edition on your machine.
Run
lms load hf-yixman--cognitivecomputations-dolphin-mistral-24b-venice-edition-gguf && lms server startUpgrade-Optionen
Hardware, die cognitivecomputations Dolphin Mistral 24B Venice Edition gut ausführt
RTX 3090 24GBGünstige WahlRTX PRO 4000 Blackwell 24GBBestes Preis-Leistungs-Verhältnis24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 178%.38.6 tok/s Dekodierung
Raises estimated decode speed by about 178%.
ca. $1,599 MSRP
RTX 4090 24GBNVIDIA-Upgrade24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 168%.37.2 tok/s Dekodierung
Raises estimated decode speed by about 168%.
ca. $1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run cognitivecomputations Dolphin Mistral 24B Venice Edition?
Yes, RTX 4000 Ada 20GB can run cognitivecomputations Dolphin Mistral 24B Venice Edition 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 Dolphin Mistral 24B Venice Edition need?
cognitivecomputations Dolphin Mistral 24B Venice Edition (24B parameters) requires approximately 20.4 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 4000 Ada 20GB?
On RTX 4000 Ada 20GB, cognitivecomputations Dolphin Mistral 24B Venice Edition 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 Dolphin Mistral 24B Venice Edition for coding?
For coding workloads, cognitivecomputations Dolphin Mistral 24B Venice Edition on RTX 4000 Ada 20GB receives a C grade with 13.9 tok/s and 14K context.
What context window can cognitivecomputations Dolphin Mistral 24B Venice Edition use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, cognitivecomputations Dolphin Mistral 24B Venice Edition 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 Dolphin Mistral 24B Venice Edition 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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