Can K EXAONE 236B A23B run on NVIDIA H200 PCIe 141GB?

Q: What should I upgrade first if K EXAONE 236B A23B feels slow on NVIDIA H200 PCIe 141GB?

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.

YES — With Q3_K_S

D39Poor

Estimated from fit model

K EXAONE 236B A23B needs ~158.6 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q3_K_S quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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.

K EXAONE 236B A23B at Q4_K_M needs 186.9 GB — too much for NVIDIA H200 PCIe 141GB (141.0 GB). Runs at Q3_K_S (158.6 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 186.9 GB, exceeds 141.0 GB available

186.9 GB required141.0 GB available

133% VRAM needed

45.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.1 tok/s

TTFT

16065 ms

Safe context

Memory

186.9 GB / 141.0 GB

Offload

20%

Memory breakdown

Weights144.0 GB

KV Cache27.7 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsK EXAONE 236B A23B on NVIDIA H200 PCIe 141GB
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: 12.1 tok/s decode · 16.1s TTFT (warm) · 30 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 12.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	14.1 tok/s	7491 ms	4K
Coding	F	Too heavy	12.1 tok/s	16065 ms	4K
Agentic Coding	F	Too heavy	9.1 tok/s	30964 ms	4K
Reasoning	F	Too heavy	12.1 tok/s	18986 ms	4K
RAG	F	Too heavy	9.1 tok/s	38706 ms	4K

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	92.0 GB	Low	C48
Q3_K_S	3	115.6 GB	Low	F0

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

NVIDIA B200 180GBBudget pick

180 GB VRAM (+39)8000 GB/s (+3200)

Makes the model fit on the accelerator instead of staying completely out of reach.36.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 199%.

~$30,000 MSRP

B100 192GBBest value

192 GB VRAM (+51)8000 GB/s (+3200)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$35,000 MSRP

NVIDIA GB200 192GBNVIDIA upgrade

192 GB VRAM (+51)8000 GB/s (+3200)

Makes the model fit on the accelerator instead of staying completely out of reach.39.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$60,000 MSRP

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run K EXAONE 236B A23B?

Yes, NVIDIA H200 PCIe 141GB can run K EXAONE 236B A23B at Q3_K_S quantization (Very compromised (needs ~12.8 GB host RAM)). The recommended Q4_K_M requires 186.9 GB which exceeds available memory, but at Q3_K_S it needs only 158.6 GB. Expected decode speed: 19.5 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 186.9 GB at Q4_K_M quantization. On NVIDIA H200 PCIe 141GB, it fits at Q3_K_S using 158.6 GB.

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

The recommended quantization is Q4_K_M, but on NVIDIA H200 PCIe 141GB the best fitting quantization is Q3_K_S, which uses 158.6 GB.

What speed will K EXAONE 236B A23B run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, K EXAONE 236B A23B achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9924ms using Q3_K_S quantization.

Can NVIDIA H200 PCIe 141GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on NVIDIA H200 PCIe 141GB receives a F grade with 12.1 tok/s and 4K context.

What context window can K EXAONE 236B A23B use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, K EXAONE 236B A23B can safely use up to 6K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GB See all hardware for K EXAONE 236B A23B

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