Can K EXAONE 236B A23B run on H100 NVL 188GB?

Q: What should I upgrade first if K EXAONE 236B A23B feels slow on H100 NVL 188GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C51Usable

Estimated from fit model

K EXAONE 236B A23B needs ~191.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 191.3 GB, 36.4 tok/s, Runs with offload (needs ~2.5 GB host RAM)

191.3 GB required188.0 GB available

102% VRAM needed

3.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~2.5 GB host RAM)

Decode

36.4 tok/s

TTFT

5323 ms

Safe context

14K

Memory

191.3 GB / 188.0 GB

Memory breakdown

Weights144.0 GB

KV Cache27.7 GB

Runtime0.9 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsK EXAONE 236B A23B on H100 NVL 188GB
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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	43.9 tok/s	2406 ms	14K
Coding	C	Runs with offload (needs ~2.5 GB host RAM)	36.4 tok/s	5323 ms	14K
Agentic Coding	D	Very compromised (needs ~20.4 GB host RAM)	29.1 tok/s	9675 ms	14K
Reasoning	C	Runs with offload (needs ~2.5 GB host RAM)	36.4 tok/s	6291 ms	14K
RAG	D	Very compromised (needs ~20.4 GB host RAM)	29.1 tok/s

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	C47
Q3_K_S	3	115.6 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run K EXAONE 236B A23B on your machine.

Run

lms load hf-lgai-exaone--k-exaone-236b-a23b-gguf && lms server start

Upgrade options

Hardware that runs K EXAONE 236B A23B well

B100 192GBBudget pick

192 GB VRAM (+4)8000 GB/s (+200)

Raises estimated decode speed by about 28%.46.7 tok/s decode

Raises estimated decode speed by about 28%.

~$35,000 MSRP

NVIDIA GB200 192GBBest value

192 GB VRAM (+4)8000 GB/s (+200)

Raises estimated decode speed by about 28%.46.7 tok/s decode

Raises estimated decode speed by about 28%.

~$60,000 MSRP

Frequently asked questions

Can H100 NVL 188GB run K EXAONE 236B A23B?

Yes, H100 NVL 188GB can run K EXAONE 236B A23B with a C grade (Runs with offload (needs ~2.5 GB host RAM)). Expected decode speed: 36.4 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 191.3 GB of memory with Q4_K_M quantization.

What is the best quantization for K EXAONE 236B A23B?

The recommended quantization for K EXAONE 236B A23B is Q4_K_M, which balances quality and memory efficiency.

What speed will K EXAONE 236B A23B run at on H100 NVL 188GB?

On H100 NVL 188GB, K EXAONE 236B A23B achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5323ms using Q4_K_M quantization.

Can H100 NVL 188GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on H100 NVL 188GB receives a C grade with 36.4 tok/s and 14K context.

What context window can K EXAONE 236B A23B use on H100 NVL 188GB?

On H100 NVL 188GB, K EXAONE 236B A23B can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if K EXAONE 236B A23B feels slow on H100 NVL 188GB?

See all results for H100 NVL 188GB See all hardware for K EXAONE 236B A23B

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