RTX 3060 12GB经济之选12 GB VRAM (+4)360 GB/s (+40)
C
Raises estimated decode speed by about 26%.49.9 tok/s 解码
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can EXAONE 3.5 7.8B Instruct run on GTX 1080 8GB?
YES — With Offload
C51Usable
Estimated from fit model
EXAONE 3.5 7.8B Instruct needs ~7.7 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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) — 7.7 GB, 39.7 tok/s, Runs with offload
7.7 GB required8.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
39.7 tok/s
TTFT
4879 ms
Safe context
22K
Memory
7.7 GB / 8.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsEXAONE 3.5 7.8B Instruct on GTX 1080 8GB
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: 39.7 tok/s decode · 4.9s TTFT (warm) · 99 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 39.7 tok/s | 2661 ms | 22K |
| Coding | C | Runs with offload | 39.7 tok/s | 4879 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 24.8 tok/s | 11364 ms | 22K |
| Reasoning | C | Runs with offload | 39.7 tok/s | 5766 ms | 22K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 24.8 tok/s | 14204 ms | 22K |
Inference speed
EXAONE 3.5 7.8B Instruct inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 109.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 109.2 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 109.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 97.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 92.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 79.4 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 78.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 78.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 50.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 49.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 46.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 41.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 40.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on GTX 1080 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 | 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 |
Get started
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 start升级选项
能流畅运行 EXAONE 3.5 7.8B Instruct 的硬件
RTX 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+8)448 GB/s (+128)
C
Raises estimated decode speed by about 47%.58.4 tok/s 解码
Raises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA 升级12 GB VRAM (+4)672 GB/s (+352)
B
Raises estimated decode speed by about 124%.89 tok/s 解码
Raises estimated decode speed by about 124%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Frequently asked questions
Can GTX 1080 8GB run EXAONE 3.5 7.8B Instruct?
Yes, GTX 1080 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs with offload). Expected decode speed: 39.7 tok/s.
How much VRAM does EXAONE 3.5 7.8B Instruct need?
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.7 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 8GB?
On GTX 1080 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 39.7 tokens per second decode speed with a time-to-first-token of 4879ms using Q4_K_M quantization.
Can GTX 1080 8GB run EXAONE 3.5 7.8B Instruct for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct on GTX 1080 8GB receives a C grade with 39.7 tok/s and 22K context.
What context window can EXAONE 3.5 7.8B Instruct use on GTX 1080 8GB?
On GTX 1080 8GB, EXAONE 3.5 7.8B Instruct can safely use up to 22K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if EXAONE 3.5 7.8B Instruct feels slow on GTX 1080 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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