RTX 3060 12GBBudget pick12 GB VRAM (+4)360 GB/s (+104)
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 3.5 7.8B Instruct i1 needs ~7.7 GB VRAM. RTX 4060 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 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
40.4 tok/s
TTFT
4798 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.
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 | 40.4 tok/s | 2617 ms | 22K |
| Coding | C | Runs with offload | 40.4 tok/s | 4798 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 26.1 tok/s | 10798 ms | 22K |
| Reasoning | C | Runs with offload | 40.4 tok/s | 5670 ms | 22K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 26.1 tok/s | 13497 ms |
How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on RTX 4060 Laptop 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 3.5 7.8B Instruct i1 on your machine.
Run
lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRaises estimated decode speed by about 45%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA upgradeRaises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Yes, RTX 4060 Laptop 8GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs with offload). Expected decode speed: 40.4 tok/s.
EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Laptop 8GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 40.4 tokens per second decode speed with a time-to-first-token of 4798ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct i1 on RTX 4060 Laptop 8GB receives a C grade with 40.4 tok/s and 22K context.
On RTX 4060 Laptop 8GB, EXAONE 3.5 7.8B Instruct i1 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-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf-on-rtx-4060-laptop-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 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.