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

YES — Runs Great

C47Usable

Estimated from fit model

EXAONE 4.0 32B needs ~42.5 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) — 42.5 GB, 344.3 tok/s, Runs well

42.5 GB required180.0 GB available

24% VRAM used

Fit status

Runs well

Decode

344.3 tok/s

TTFT

562 ms

Safe context

603K

Memory

42.5 GB / 180.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 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
>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: 344.3 tok/s decode · 562ms TTFT (warm) · 861 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	C	Runs well	344.3 tok/s	350 ms	603K
Coding	C	Runs well	344.3 tok/s	562 ms	603K
Agentic Coding	C	Runs well	344.3 tok/s	818 ms	603K
Reasoning	C	Runs well	344.3 tok/s	665 ms	603K
RAG	C	Runs well	344.3 tok/s	1022 ms	603K

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 ~62 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	61.5	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	30.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	30.8	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	28.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	23.2	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	22.5	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	21.4	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	19.8	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.4	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	8.4	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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	D37
Q3_K_S	3	15.7 GB	Low	D37
NVFP4	4	17.9 GB	Medium	D37
Q4_K_M	4	19.5 GB	Medium	D37
Q5_K_M	5	23.0 GB	High	D38
Q6_K	6	26.2 GB	High	D38
Q8_0	8	34.2 GB	Very High	D39
F16Best for your GPU	16	65.6 GB	Maximum	C43

Get started

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

Run

lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server start

Frequently asked questions

Can NVIDIA B200 180GB run EXAONE 4.0 32B?

Yes, NVIDIA B200 180GB can run EXAONE 4.0 32B with a C 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 42.5 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 C grade with 344.3 tok/s and 603K 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 603K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-32b-gguf-on-b200-180gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: