RTX 3060 12GB经济之选12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.49.9 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can EXAONE 3.5 7.8B Instruct run on RTX 5060 8GB?
YES — With Offload
C52Usable
Estimated from fit model
EXAONE 3.5 7.8B Instruct needs ~7.7 GB VRAM. RTX 5060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 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, 57.4 tok/s, Runs with offload
7.7 GB required8.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
57.4 tok/s
TTFT
3371 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 RTX 5060 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: 57.4 tok/s decode · 3.4s TTFT (warm) · 144 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.
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 | 57.4 tok/s | 1839 ms | 22K |
| Coding | C | Runs with offload | 57.4 tok/s | 3371 ms | 22K |
| Agentic Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 38.0 tok/s | 7406 ms | 22K |
| Reasoning | C | Runs with offload | 57.4 tok/s | 3984 ms | 22K |
| RAG | C | Runs with offload (needs ~0.3 GB host RAM) | 38.0 tok/s | 9258 ms | 22K |
Quantization options
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RTX 5060 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 5070 12GB最佳性价比12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 55%.89 tok/s 解码
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA 升级12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 42%.81.6 tok/s 解码
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 5060 8GB run EXAONE 3.5 7.8B Instruct?
Yes, RTX 5060 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs with offload). Expected decode speed: 57.4 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 RTX 5060 8GB?
On RTX 5060 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3371ms using Q4_K_M quantization.
Can RTX 5060 8GB run EXAONE 3.5 7.8B Instruct for coding?
For coding workloads, EXAONE 3.5 7.8B Instruct on RTX 5060 8GB receives a C grade with 57.4 tok/s and 22K context.
What context window can EXAONE 3.5 7.8B Instruct use on RTX 5060 8GB?
On RTX 5060 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 RTX 5060 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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