Can EXAONE 4.0 32B run on NVIDIA B200 180GB?

YES — Runs Great

A82Great

Estimated from fit model

EXAONE 4.0 32B needs ~42.6 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~344 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 42.6 GB, 371.8 tok/s, Runs well

42.6 GB required180.0 GB available

24% VRAM used

Fit status

Runs well

Decode

371.8 tok/s

TTFT

521 ms

Safe context

131K

Memory

42.6 GB / 180.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on NVIDIA B200 180GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 371.8 tok/s decode · 521ms TTFT (warm) · 930 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	A	Runs well	371.8 tok/s	350 ms	131K
Coding	A	Runs well	344.3 tok/s	562 ms	131K
Agentic Coding	A	Runs well	371.8 tok/s	757 ms	131K
Reasoning	A	Runs well	371.8 tok/s	615 ms	131K
RAG	A	Runs well	371.8 tok/s	947 ms	131K

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 ~66 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	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A72
Q3_K_S	3	15.7 GB	Low	A72
NVFP4	4

Get started

Copy-paste commands to run EXAONE 4.0 32B on your machine.

Run

ollama run exaone-4:32b

Your hardware

More models your NVIDIA B200 180GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	97.4 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can NVIDIA B200 180GB run EXAONE 4.0 32B?

Yes, NVIDIA B200 180GB can run EXAONE 4.0 32B with a A grade (Runs well). Expected decode speed: 344.3 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 42.6 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, EXAONE 4.0 32B achieves approximately 344.3 tokens per second decode speed with a time-to-first-token of 562ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on NVIDIA B200 180GB receives a A grade with 344.3 tok/s and 131K context.

What context window can EXAONE 4.0 32B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, EXAONE 4.0 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for EXAONE 4.0 32B

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