MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.78.8 tok/s decodificación
~$2,499 MSRP
Can exaone 3.0 7.8b it run on NVIDIA V100 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
exaone 3.0 7.8b it needs ~10.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~109 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 10.1 GB, 109.2 tok/s, Runs well
10.1 GB required32.0 GB available
32% VRAM used
Fit status
Runs well
Decode
109.2 tok/s
TTFT
1773 ms
Safe context
400K
Memory
10.1 GB / 32.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it on NVIDIA V100 32GB
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: 109.2 tok/s decode · 1.8s TTFT (warm) · 273 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 109.2 tok/s | 967 ms | 400K |
| Coding | C | Runs well | 109.2 tok/s | 1773 ms | 400K |
| Agentic Coding | C | Runs well | 109.2 tok/s | 2579 ms | 400K |
| Reasoning | C | Runs well | 109.2 tok/s | 2095 ms | 400K |
| RAG | C | Runs well | 109.2 tok/s | 3223 ms | 400K |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C43 |
Q3_K_S | 3 | 3.8 GB | Low | C43 |
NVFP4 | 4 | 4.4 GB | Medium | C43 |
Q4_K_M | 4 | 4.8 GB | Medium | C43 |
Q5_K_M | 5 | 5.6 GB | High | C44 |
Q6_K | 6 | 6.4 GB | High | C44 |
Q8_0 | 8 | 8.3 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C48 |
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 startOpciones de mejora
Hardware que ejecuta bien exaone 3.0 7.8b it
Frequently asked questions
Can NVIDIA V100 32GB run exaone 3.0 7.8b it?
Yes, NVIDIA V100 32GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 109.2 tok/s.
How much VRAM does exaone 3.0 7.8b it need?
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.
What is the best quantization for exaone 3.0 7.8b it?
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
What speed will exaone 3.0 7.8b it run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, exaone 3.0 7.8b it achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run exaone 3.0 7.8b it for coding?
For coding workloads, exaone 3.0 7.8b it on NVIDIA V100 32GB receives a C grade with 109.2 tok/s and 400K context.
What context window can exaone 3.0 7.8b it use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, exaone 3.0 7.8b it can safely use up to 400K 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-bingsu--exaone-3-0-7-8b-it-on-v100-32gb" 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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