RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.49.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
exaone 3.0 7.8b it needs ~7.7 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 tok/s.
Operating mode
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
Fit status
Runs with offload
Decode
57.4 tok/s
TTFT
3371 ms
Safe context
22K
Memory
7.7 GB / 8.0 GB
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 57.4 tok/s | 1839 ms | 22K |
| Coding | C | Runs with offload | 57.4 tok/s | 3371 ms | 22K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 35.9 tok/s | 7851 ms | 22K |
| Reasoning | C | Runs with offload | 57.4 tok/s | 3984 ms | 22K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 35.9 tok/s | 9813 ms |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on RTX 2070 Super 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 |
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 startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 2070 Super 8GB can run exaone 3.0 7.8b it with a C grade (Runs with offload). Expected decode speed: 57.4 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
On RTX 2070 Super 8GB, exaone 3.0 7.8b it achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3371ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on RTX 2070 Super 8GB receives a C grade with 57.4 tok/s and 22K context.
On RTX 2070 Super 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bingsu--exaone-3-0-7-8b-it-on-rtx-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 22K |
| 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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.