RTX 3090 24GB经济之选24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 85%.109.2 tok/s 解码
Raises estimated decode speed by about 85%.
~$1,499 MSRP
Can exaone 3.0 7.8b it run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
exaone 3.0 7.8b it needs ~8.9 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~59 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.9 GB, 59.0 tok/s, Runs well
8.9 GB required20.0 GB available
45% VRAM used
Fit status
Runs well
Decode
59.0 tok/s
TTFT
3280 ms
Safe context
211K
Memory
8.9 GB / 20.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it 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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 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 | 59.0 tok/s | 1789 ms | 211K |
| Coding | C | Runs well | 59.0 tok/s | 3280 ms | 211K |
| Agentic Coding | C | Runs well | 59.0 tok/s | 4772 ms | 211K |
| Reasoning | C | Runs well | 59.0 tok/s | 3877 ms | 211K |
| RAG | C | Runs well | 59.0 tok/s | 5964 ms | 211K |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C45 |
Q3_K_S | 3 | 3.8 GB | Low | C46 |
NVFP4 | 4 | 4.4 GB | Medium | C46 |
Q4_K_M | 4 | 4.8 GB | Medium | C46 |
Q5_K_M | 5 | 5.6 GB | High | C47 |
Q6_K | 6 | 6.4 GB | High | C48 |
Q8_0 | 8 | 8.3 GB | Very High | C49 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C50 |
Get started
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start升级选项
能流畅运行 exaone 3.0 7.8b it 的硬件
RTX 3090 24GB经济之选RTX 4090 24GB最佳性价比24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 85%.109.2 tok/s 解码
Raises estimated decode speed by about 85%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA 升级24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 85%.109.2 tok/s 解码
Raises estimated decode speed by about 85%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run exaone 3.0 7.8b it?
Yes, RTX 4000 Ada 20GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 59.0 tok/s.
How much VRAM does exaone 3.0 7.8b it need?
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
What is the best quantization for exaone 3.0 7.8b it?
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
What speed will exaone 3.0 7.8b it run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, exaone 3.0 7.8b it achieves approximately 59.0 tokens per second decode speed with a time-to-first-token of 3280ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run exaone 3.0 7.8b it for coding?
For coding workloads, exaone 3.0 7.8b it on RTX 4000 Ada 20GB receives a C grade with 59.0 tok/s and 211K context.
What context window can exaone 3.0 7.8b it use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, exaone 3.0 7.8b it can safely use up to 211K 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-bingsu--exaone-3-0-7-8b-it-on-rtx-4000-ada-20gb" 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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