RTX A6000 48GBGünstige Wahl48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.29.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $4,650 MSRP
Can EXAONE 4.0 32B run on NVIDIA V100 32GB?
YES — Tight Fit
C50Usable
Estimated from fit model
EXAONE 4.0 32B needs ~27.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 27.7 GB, 30.9 tok/s, Tight fit
27.7 GB required32.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
30.9 tok/s
TTFT
6267 ms
Safe context
34K
Memory
27.7 GB / 32.0 GB
Memory breakdown
Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsEXAONE 4.0 32B 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: 30.9 tok/s decode · 6.3s TTFT (warm) · 77 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 | 30.9 tok/s | 3418 ms | 34K |
| Coding | C | Tight fit | 30.9 tok/s | 6267 ms | 34K |
| Agentic Coding | C | Runs with offload | 30.9 tok/s | 9116 ms | 34K |
| Reasoning | C | Tight fit | 30.9 tok/s | 7407 ms | 34K |
| RAG | C | Runs with offload | 30.9 tok/s | 11395 ms | 34K |
Quantization options
How EXAONE 4.0 32B (32B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C47 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_M | 4 | 19.5 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 23.0 GB | High | C48 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run EXAONE 4.0 32B on your machine.
Run
lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server startUpgrade-Optionen
Hardware, die EXAONE 4.0 32B gut ausführt
RTX A6000 48GBGünstige WahlRTX PRO 5000 Blackwell 48GBBestes Preis-Leistungs-Verhältnis48 GB VRAM (+16)1344 GB/s (+444)
C
Raises estimated decode speed by about 87%.57.8 tok/s Dekodierung
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
NVIDIA L40 48GBNVIDIA-Upgrade48 GB VRAM (+16)
C
Adds memory headroom for longer context windows and future model growth.34.5 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $5,500 MSRP
Frequently asked questions
Can NVIDIA V100 32GB run EXAONE 4.0 32B?
Yes, NVIDIA V100 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 30.9 tok/s.
How much VRAM does EXAONE 4.0 32B need?
EXAONE 4.0 32B (32B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
What is the best quantization for EXAONE 4.0 32B?
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
What speed will EXAONE 4.0 32B run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, EXAONE 4.0 32B achieves approximately 30.9 tokens per second decode speed with a time-to-first-token of 6267ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run EXAONE 4.0 32B for coding?
For coding workloads, EXAONE 4.0 32B on NVIDIA V100 32GB receives a C grade with 30.9 tok/s and 34K context.
What context window can EXAONE 4.0 32B use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, EXAONE 4.0 32B can safely use up to 34K 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-lgai-exaone--exaone-4-0-32b-gguf-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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