Will It Run AI

Can EXAONE 4.0 32B run on NVIDIA A10 24GB?

BARELY — Tight on Memory

D38Poor
Estimated from fit model

EXAONE 4.0 32B needs ~26.6 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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, 14.5 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

14.5 tok/s

TTFT

13338 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 NVIDIA A10 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: 14.5 tok/s decode · 13.3s TTFT (warm) · 36 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 1.9 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)16.9 tok/s6236 ms5K
CodingDVery compromised (needs ~1.9 GB host RAM)14.5 tok/s13338 ms5K
Agentic CodingFToo heavy11.0 tok/s25615 ms5K
ReasoningDVery compromised (needs ~1.9 GB host RAM)14.5 tok/s15763 ms5K
RAGFToo heavy11.0 tok/s32019 ms5K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on NVIDIA A10 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

Opções de upgrade

Hardware que roda bem EXAONE 4.0 32B

NVIDIARTX 5090 32GBOpção econômica
32 GB VRAM (+8)1792 GB/s (+1192)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.39.9 tok/s decodificação

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

Raises estimated decode speed by about 175%.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMelhor custo-benefício
32 GB VRAM (+8)896 GB/s (+296)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.38.6 tok/s decodificação

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

Raises estimated decode speed by about 166%.

~$2,499 MSRP

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

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

Raises estimated decode speed by about 63%.

~$4,000 MSRP

Frequently asked questions

Can NVIDIA A10 24GB run EXAONE 4.0 32B?

Yes, NVIDIA A10 24GB can run EXAONE 4.0 32B with a D grade (Very compromised (needs ~1.9 GB host RAM)). Expected decode speed: 14.5 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, EXAONE 4.0 32B achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13338ms using Q4_K_M quantization.

Can NVIDIA A10 24GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on NVIDIA A10 24GB receives a D grade with 14.5 tok/s and 5K context.

What context window can EXAONE 4.0 32B use on NVIDIA A10 24GB?

On NVIDIA A10 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 NVIDIA A10 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 NVIDIA A10 24GBSee all hardware for EXAONE 4.0 32B
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