EXAONE 4.0 32B needs ~26.6 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Operating mode
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
2.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.9 GB host RAM)
Decode
18.6 tok/s
TTFT
10420 ms
Safe context
5K
Memory
26.6 GB / 24.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.5 GB host RAM) | 21.7 tok/s | 4872 ms | 5K |
| Coding | D | Very compromised | 18.6 tok/s | 10420 ms | 5K |
| Agentic Coding | F | Too heavy | 14.1 tok/s | 20012 ms | 5K |
| Reasoning | D | Very compromised (needs ~1.9 GB host RAM) | 18.6 tok/s | 12315 ms | 5K |
| RAG | F | Too heavy | 14.1 tok/s | 25015 ms |
How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C50 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4Best for your GPU |
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
RTX 5090 32GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 115%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 108%.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 27%.
~$4,000 MSRP
Yes, RTX A5500 24GB can run EXAONE 4.0 32B with a D grade (Very compromised). Expected decode speed: 18.6 tok/s.
EXAONE 4.0 32B (32B parameters) requires approximately 26.6 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX A5500 24GB, EXAONE 4.0 32B achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10420ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 32B on RTX A5500 24GB receives a D grade with 18.6 tok/s and 5K context.
On RTX A5500 24GB, EXAONE 4.0 32B can safely use up to 5K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-32b-gguf-on-rtx-a5500-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 5K |
| 4 |
17.9 GB |
| Medium |
| C49 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.