RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+88)
C
Adds memory headroom for longer context windows and future model growth.49.9 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can exaone 3.0 7.8b it run on RTX 4060 8GB?
YES — With Offload
C51Usable
Estimated from fit model
exaone 3.0 7.8b it needs ~7.7 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: 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) — 7.7 GB, 41.7 tok/s, Runs with offload
7.7 GB required8.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
41.7 tok/s
TTFT
4639 ms
Safe context
22K
Memory
7.7 GB / 8.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it on RTX 4060 8GB
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: 41.7 tok/s decode · 4.6s TTFT (warm) · 104 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 | 41.7 tok/s | 2530 ms | 22K |
| Coding | C | Runs with offload | 41.7 tok/s | 4639 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 27.0 tok/s | 10441 ms | 22K |
| Reasoning | C | Runs with offload | 41.7 tok/s | 5483 ms | 22K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 27.0 tok/s | 13051 ms | 22K |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on RTX 4060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C54 |
Q3_K_S | 3 | 3.8 GB | Low | C53 |
NVFP4 | 4 | 4.4 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.8 GB | Medium | C53 |
Q5_K_M | 5 | 5.6 GB | High | F0 |
Q6_K | 6 | 6.4 GB | High | F0 |
Q8_0 | 8 | 8.3 GB | Very High | F0 |
F16 | 16 | 16.0 GB | Maximum | F0 |
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アップグレードオプション
exaone 3.0 7.8b itを快適に動かすハードウェア
RTX 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+8)448 GB/s (+176)
C
Raises estimated decode speed by about 40%.58.4 tok/sデコード
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RTX 5070 12GBNVIDIAアップグレード12 GB VRAM (+4)672 GB/s (+400)
B
Raises estimated decode speed by about 113%.89 tok/sデコード
Raises estimated decode speed by about 113%.
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
Frequently asked questions
Can RTX 4060 8GB run exaone 3.0 7.8b it?
Yes, RTX 4060 8GB can run exaone 3.0 7.8b it with a C grade (Runs with offload). Expected decode speed: 41.7 tok/s.
How much VRAM does exaone 3.0 7.8b it need?
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.7 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 4060 8GB?
On RTX 4060 8GB, exaone 3.0 7.8b it achieves approximately 41.7 tokens per second decode speed with a time-to-first-token of 4639ms using Q4_K_M quantization.
Can RTX 4060 8GB run exaone 3.0 7.8b it for coding?
For coding workloads, exaone 3.0 7.8b it on RTX 4060 8GB receives a C grade with 41.7 tok/s and 22K context.
What context window can exaone 3.0 7.8b it use on RTX 4060 8GB?
On RTX 4060 8GB, exaone 3.0 7.8b it can safely use up to 22K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if exaone 3.0 7.8b it feels slow on RTX 4060 8GB?
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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<iframe src="https://willitrunai.com/embed/hf-bingsu--exaone-3-0-7-8b-it-on-rtx-4060-8gb" 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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