Will It Run AI

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
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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) — 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload46.7 tok/s2262 ms10K
CodingCRuns with offload (needs ~7.9 GB host RAM)36.3 tok/s5340 ms10K
Agentic CodingFToo heavy29.0 tok/s9714 ms10K
ReasoningCRuns with offload (needs ~7.9 GB host RAM)36.3 tok/s6311 ms10K
RAGFToo heavy29.0 tok/s12143 ms10K

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
92.0 GB
LowC47
Q3_K_S
3
115.6 GB
LowC48
NVFP4
4
132.2 GB
MediumC48
Q4_K_MBest for your GPU
4
144.0 GB
MediumC48
Q5_K_M
5
169.9 GB
HighF0
Q6_K
6
193.5 GB
HighF0
Q8_0
8
252.5 GB
Very HighF0
F16
16
483.8 GB
MaximumF0

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

アップグレードオプション

K EXAONE 236B A23Bを快適に動かすハードウェア

NVIDIAB100 192GBコスパ重視
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.7 tok/sデコード

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

Raises estimated decode speed by about 29%.

〜$35,000 MSRP

NVIDIANVIDIA GB200 192GBコスパ最良
192 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46.7 tok/sデコード

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

Raises estimated decode speed by about 29%.

〜$60,000 MSRP

NVIDIAH100 NVL 188GBNVIDIAアップグレード
188 GB VRAM (+8)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.36.4 tok/sデコード

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 180GBSee all hardware for K EXAONE 236B A23B
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