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URL: https://willitrunai.com/can-run/hf-lgai-exaone--exaone-4-0-32b-gguf-on-quadro-rtx-8000-48gb

⇱ EXAONE 4.0 32B on Quadro RTX 8000 48GB? YES

Can EXAONE 4.0 32B run on Quadro RTX 8000 48GB?

YES — Runs Great

C50Usable
○Estimated from fit model

EXAONE 4.0 32B needs ~29.3 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:

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) — 29.3 GB, 23.8 tok/s, Runs well
29.3 GB required48.0 GB available
61% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8150 ms

Safe context

96K

Memory

29.3 GB / 48.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Quadro RTX 8000 48GB
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: 23.8 tok/s decode · 8.2s TTFT (warm) · 59 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well23.8 tok/s4446 ms96K
CodingCRuns well23.8 tok/s8150 ms96K
Agentic CodingCRuns well23.8 tok/s11855 ms96K
ReasoningCRuns well23.8 tok/s9632 ms96K
RAGCRuns well23.8 tok/s14818 ms96K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC44
Q3_K_S
3
15.7 GB
LowC45
NVFP4
4
17.9 GB
MediumC45
Q4_K_M
4
19.5 GB
MediumC46
Q5_K_M
5
23.0 GB
HighC47
Q6_K
6
26.2 GB
HighC48
Q8_0Best for your GPU
8
34.2 GB
Very HighC48
F16
16
65.6 GB
MaximumF0

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

Upgrade options

Hardware that runs EXAONE 4.0 32B well

AMD Instinct MI210 64GBBudget pick
64 GB VRAM (+16)1638 GB/s (+966)
C
Raises estimated decode speed by about 140%.57.1 tok/s decode

Raises estimated decode speed by about 140%.

Adds memory headroom for longer context windows and future model growth.

~$10,000 MSRP

Frequently asked questions

See all results for Quadro RTX 8000 48GBSee all hardware for EXAONE 4.0 32B