48 GB VRAM (+16)768 GB/s (+192)
C
Raises estimated decode speed by about 27%.29.9 tok/s decode
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
Can EXAONE 4.0 32B run on RTX 5000 Ada 32GB?
YES — Tight Fit
C49Usable
○Estimated from fit model
EXAONE 4.0 32B needs ~27.7 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
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) — 27.7 GB, 23.6 tok/s, Tight fit
27.7 GB required32.0 GB available
87% VRAM used
Fit status
Tight fit
Decode
23.6 tok/s
TTFT
8201 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 RTX 5000 Ada 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: 23.6 tok/s decode · 8.2s TTFT (warm) · 59 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 | 23.6 tok/s | 4473 ms | 34K |
| Coding | C | Tight fit | 23.6 tok/s | 8201 ms | 34K |
| Agentic Coding | C | Runs with offload | 23.6 tok/s | 11929 ms | 34K |
| Reasoning | C | Tight fit | 23.6 tok/s | 9692 ms | 34K |
| RAG | C | Runs with offload | 23.6 tok/s | 14911 ms | 34K |
Quantization options
How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX 5000 Ada 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 |
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 options
Hardware that runs EXAONE 4.0 32B well
48 GB VRAM (+16)1344 GB/s (+768)
C
Raises estimated decode speed by about 145%.57.8 tok/s decode
Raises estimated decode speed by about 145%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
48 GB VRAM (+16)864 GB/s (+288)
C
Raises estimated decode speed by about 46%.34.5 tok/s decode
Raises estimated decode speed by about 46%.
Adds memory headroom for longer context windows and future model growth.
~$5,500 MSRP