RTX 5070 12GBOpción económica12 GB VRAM (+1)672 GB/s (+188)
B
Sube la velocidad estimada de decodificación alrededor de un 48%.89 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 48%.
~$549 MSRP
Can EXAONE 3.5 7.8B Instruct run on GTX 1080 Ti 11GB?
YES — Runs Great
B55Good
Estimated from fit model
EXAONE 3.5 7.8B Instruct needs ~8.0 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~60 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.0 GB, 60.0 tok/s, Runs well
8.0 GB required11.0 GB available
73% VRAM used
Fit status
Runs well
Decode
60.0 tok/s
TTFT
3226 ms
Safe context
69K
Memory
8.0 GB / 11.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 7.8B Instruct on GTX 1080 Ti 11GB
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: 60.0 tok/s decode · 3.2s TTFT (warm) · 150 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 60.0 tok/s | 1760 ms | 69K |
| Coding | B | Runs well | 60.0 tok/s | 3226 ms | 69K |
| Agentic Coding | B | Runs well | 60.0 tok/s | 4692 ms | 69K |
| Reasoning | B | Runs well | 60.0 tok/s | 3812 ms | 69K |
| RAG | B | Runs well | 60.0 tok/s | 5865 ms | 69K |
Quantization options
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C50 |
Q3_K_S | 3 | 3.8 GB | Low | C51 |
NVFP4 | 4 | 4.4 GB | Medium | C52 |
Q4_K_M | 4 | 4.8 GB | Medium | C53 |
Q5_K_M | 5 | 5.6 GB | High | C52 |
Q6_KBest for your GPU | 6 | 6.4 GB | High | C52 |
Q8_0 | 8 | 8.3 GB | Very High | F0 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien EXAONE 3.5 7.8B Instruct
RTX 5070 12GBOpción económicaRTX 4070 Super 12GBMejor relación calidad-precio12 GB VRAM (+1)504 GB/s (+20)
B
Sube la velocidad estimada de decodificación alrededor de un 36%.81.6 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 36%.
~$599 MSRP
RTX 4070 12GBMejora NVIDIA12 GB VRAM (+1)504 GB/s (+20)
B
Sube la velocidad estimada de decodificación alrededor de un 32%.79.4 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 32%.
~$599 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct?
Yes, GTX 1080 Ti 11GB can run EXAONE 3.5 7.8B Instruct with a B grade (Runs well). Expected decode speed: 60.0 tok/s.
How much VRAM does EXAONE 3.5 7.8B Instruct need?
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 3.5 7.8B Instruct?
The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 3.5 7.8B Instruct run at on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct achieves approximately 60.0 tokens per second decode speed with a time-to-first-token of 3226ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run EXAONE 3.5 7.8B Instruct for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct on GTX 1080 Ti 11GB receives a B grade with 60.0 tok/s and 69K context.
What context window can EXAONE 3.5 7.8B Instruct use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, EXAONE 3.5 7.8B Instruct can safely use up to 69K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf-on-gtx-1080-ti-11gb" 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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