12 GB VRAM (+4)360 GB/s (+136)
C
Raises estimated decode speed by about 26%.49.9 tok/s decode
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can exaone 3.0 7.8b it run on RTX 5050 8GB?
YES — With Offload
C51Usable
○Estimated from fit model
exaone 3.0 7.8b it needs ~7.7 GB VRAM. RTX 5050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
Share:
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, 39.5 tok/s, Runs with offload
7.7 GB required8.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
39.5 tok/s
TTFT
4896 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 3.0 7.8b it on RTX 5050 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: 39.5 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.
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 | 39.5 tok/s | 2670 ms | 22K |
| Coding | C | Runs with offload | 39.5 tok/s | 4896 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 10757 ms | 22K |
| Reasoning | C | Runs with offload | 39.5 tok/s | 5786 ms | 22K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 26.2 tok/s | 13446 ms |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on RTX 5050 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 |
Get started
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startUpgrade options
Hardware that runs exaone 3.0 7.8b it well
16 GB VRAM (+8)448 GB/s (+224)
C
Raises estimated decode speed by about 48%.58.4 tok/s decode
Raises estimated decode speed by about 48%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
12 GB VRAM (+4)672 GB/s (+448)
B
Raises estimated decode speed by about 125%.89 tok/s decode
Raises estimated decode speed by about 125%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP