Will It Run AI

Can EXAONE 4.0 32B run on RTX A5500 24GB?

BARELY — Tight on Memory

D39Poor
Estimated from fit model

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.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.6 GB, 18.6 tok/s, Very compromised (needs ~1.9 GB host RAM)
26.6 GB required24.0 GB available
111% VRAM needed

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%

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on RTX A5500 24GB
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: 18.6 tok/s decode · 10.4s TTFT (warm) · 46 tok/s prefill

What limits this setup

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.

Best improvement path

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.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0.5 GB host RAM)21.7 tok/s4872 ms5K
CodingDVery compromised18.6 tok/s10420 ms5K
Agentic CodingFToo heavy14.1 tok/s20012 ms5K
ReasoningDVery compromised (needs ~1.9 GB host RAM)18.6 tok/s12315 ms5K
RAGFToo heavy14.1 tok/s25015 ms5K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC50
Q3_K_S
3
15.7 GB
LowC49
NVFP4Best for your GPU
4
17.9 GB
MediumC49
Q4_K_M
4
19.5 GB
MediumF0
Q5_K_M
5
23.0 GB
HighF0
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
F16
16
65.6 GB
MaximumF0

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 start

升级选项

能流畅运行 EXAONE 4.0 32B 的硬件

NVIDIARTX 5090 32GB经济之选
32 GB VRAM (+8)1792 GB/s (+1024)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.39.9 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 115%.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GB最佳性价比
32 GB VRAM (+8)896 GB/s (+128)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.38.6 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 108%.

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBNVIDIA 升级
32 GB VRAM (+8)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.23.6 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 27%.

~$4,000 MSRP

Frequently asked questions

Can RTX A5500 24GB run EXAONE 4.0 32B?

Yes, RTX A5500 24GB can run EXAONE 4.0 32B with a D grade (Very compromised). Expected decode speed: 18.6 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 26.6 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 RTX A5500 24GB?

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.

Can RTX A5500 24GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on RTX A5500 24GB receives a D grade with 18.6 tok/s and 5K context.

What context window can EXAONE 4.0 32B use on RTX A5500 24GB?

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.

What should I upgrade first if EXAONE 4.0 32B feels slow on RTX A5500 24GB?

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.

See all results for RTX A5500 24GBSee all hardware for EXAONE 4.0 32B
Embed this result▼

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: