EXAONE 3.5 7.8B Instruct needs ~8.1 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~109 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 well
Decode
109.2 tok/s
TTFT
1773 ms
Safe context
85K
Memory
8.1 GB / 12.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 109.2 tok/s | 967 ms | 85K |
| Coding | B | Runs well | 109.2 tok/s | 1773 ms | 85K |
| Agentic Coding | B | Runs well | 109.2 tok/s | 2579 ms | 85K |
| Reasoning | B | Runs well | 109.2 tok/s | 2095 ms | 85K |
| RAG | B | Runs well | 109.2 tok/s | 3223 ms | 85K |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C49 |
Q3_K_S | 3 | 3.8 GB | Low | C50 |
NVFP4 | 4 | 4.4 GB | Medium | C51 |
Q4_K_M | 4 | 4.8 GB | Medium | C51 |
Q5_K_M | 5 | 5.6 GB | High | C52 |
Q6_K | 6 | 6.4 GB | High | C52 |
Q8_0Best for your GPU | 8 | 8.3 GB | Very High | C51 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.
Run
lms load hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf && lms server startYes, RTX 3080 Ti 12GB can run EXAONE 3.5 7.8B Instruct with a B grade (Runs well). Expected decode speed: 109.2 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.1 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 3080 Ti 12GB, EXAONE 3.5 7.8B Instruct achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 3080 Ti 12GB receives a B grade with 109.2 tok/s and 85K context.
On RTX 3080 Ti 12GB, EXAONE 3.5 7.8B Instruct can safely use up to 85K 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-lgai-exaone--exaone-3-5-7-8b-instruct-gguf-on-rtx-3080-ti-12gb" 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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