Can K EXAONE 236B A23B run on NVIDIA B200 180GB?

YES — With Offload

C41Usable

Estimated from fit model

K EXAONE 236B A23B needs ~190.5 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 190.5 GB, 36.3 tok/s, Runs with offload (needs ~7.9 GB host RAM)

190.5 GB required180.0 GB available

106% VRAM needed

10.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~7.9 GB host RAM)

Decode

36.3 tok/s

TTFT

5340 ms

Safe context

10K

Memory

190.5 GB / 180.0 GB

Offload

10%

Memory breakdown

Weights144.0 GB

KV Cache27.7 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsK EXAONE 236B A23B on NVIDIA B200 180GB
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: 36.3 tok/s decode · 5.3s TTFT (warm) · 91 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 7.9 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	C	Runs with offload	46.7 tok/s	2262 ms	10K
Coding	C	Runs with offload (needs ~7.9 GB host RAM)	36.3 tok/s	5340 ms	10K
Agentic Coding	F	Too heavy	29.0 tok/s	9714 ms	10K
Reasoning	C	Runs with offload (needs ~7.9 GB host RAM)	36.3 tok/s	6311 ms	10K
RAG	F	Too heavy	29.0 tok/s	12143 ms	10K

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on NVIDIA B200 180GB (180.0 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	132.2 GB	Medium	C48
Q4_K_MBest for your GPU	4	144.0 GB	Medium	C48
Q5_K_M	5	169.9 GB	High	F0
Q6_K	6	193.5 GB	High	F0
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	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

升级选项

能流畅运行 K EXAONE 236B A23B 的硬件

B100 192GB经济之选

192 GB VRAM (+12)

Removes host-memory offload, which is usually the single biggest latency and throughput win.46.7 tok/s 解码

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

Raises estimated decode speed by about 29%.

~$35,000 MSRP

NVIDIA GB200 192GB最佳性价比

192 GB VRAM (+12)

Removes host-memory offload, which is usually the single biggest latency and throughput win.46.7 tok/s 解码

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

Raises estimated decode speed by about 29%.

~$60,000 MSRP

H100 NVL 188GBNVIDIA 升级

188 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.36.4 tok/s 解码

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

~$60,000 MSRP

Frequently asked questions

Can NVIDIA B200 180GB run K EXAONE 236B A23B?

Yes, NVIDIA B200 180GB can run K EXAONE 236B A23B with a C grade (Runs with offload (needs ~7.9 GB host RAM)). Expected decode speed: 36.3 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 190.5 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, K EXAONE 236B A23B achieves approximately 36.3 tokens per second decode speed with a time-to-first-token of 5340ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on NVIDIA B200 180GB receives a C grade with 36.3 tok/s and 10K context.

What context window can K EXAONE 236B A23B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, K EXAONE 236B A23B can safely use up to 10K 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 NVIDIA B200 180GB?

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 B200 180GB See all hardware for K EXAONE 236B A23B

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