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

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) — 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

Inference speed

EXAONE 4.0 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 4.0 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~62 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	61.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.8	Fits

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 start

Upgrade options

Hardware that runs EXAONE 4.0 32B well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)768 GB/s (+192)

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

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+768)

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

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+16)864 GB/s (+288)

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

Frequently asked questions

Can RTX 5000 Ada 32GB run EXAONE 4.0 32B?

Yes, RTX 5000 Ada 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 23.6 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, EXAONE 4.0 32B achieves approximately 23.6 tokens per second decode speed with a time-to-first-token of 8201ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on RTX 5000 Ada 32GB receives a C grade with 23.6 tok/s and 34K context.

What context window can EXAONE 4.0 32B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 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.

See all results for RTX 5000 Ada 32GB See all hardware for EXAONE 4.0 32B

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