EXAONE 3.5 7.8B Instruct needs ~7.2 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~20 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
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.8 GB host RAM)
Decode
19.8 tok/s
TTFT
9757 ms
Safe context
4K
Memory
7.2 GB / 6.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.5 GB host RAM) | 22.9 tok/s | 4606 ms | 4K |
| Coding | D | Very compromised (needs ~0.8 GB host RAM) | 19.8 tok/s | 9757 ms | 4K |
| Agentic Coding | F | Too heavy | 15.2 tok/s | 18469 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.8 GB host RAM) | 19.8 tok/s | 11531 ms | 4K |
| RAG | F | Too heavy | 15.2 tok/s | 23087 ms |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.0 GB | Low | C54 |
Q3_K_S | 3 | 3.8 GB | Low | F0 |
Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server startUpgrade options
RTX 3050 8GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 32%.
~$249 MSRP
RTX 5060 8GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 190%.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 99%.
~$299 MSRP
Yes, RTX 2060 6GB can run EXAONE 3.5 7.8B Instruct with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 19.8 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.2 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 6GB, EXAONE 3.5 7.8B Instruct achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9757ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 2060 6GB receives a D grade with 19.8 tok/s and 4K context.
On RTX 2060 6GB, EXAONE 3.5 7.8B Instruct can safely use up to 4K 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-lmstudio-community--exaone-3-5-7-8b-instruct-gguf-on-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
4.4 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.8 GB | Medium | F0 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.