Will It Run AI

Can EXAONE 4.0 32B run on Radeon Pro W6800 32GB?

YES — Tight Fit

C48Usable
Estimated from fit model

EXAONE 4.0 32B needs ~27.4 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 27.4 GB, 14.7 tok/s, Tight fit
27.4 GB required32.0 GB available
86% VRAM used

Fit status

Tight fit

Decode

14.7 tok/s

TTFT

13180 ms

Safe context

36K

Memory

27.4 GB / 32.0 GB

Memory breakdown

Weights19.5 GB
KV Cache3.8 GB
Runtime0.9 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Radeon Pro W6800 32GB
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: 14.7 tok/s decode · 13.2s TTFT (warm) · 37 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well14.7 tok/s7189 ms36K
CodingCTight fit14.7 tok/s13180 ms36K
Agentic CodingCRuns with offload14.7 tok/s19171 ms36K
ReasoningCTight fit14.7 tok/s15577 ms36K
RAGCRuns with offload14.7 tok/s23964 ms36K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC47
Q3_K_S
3
15.7 GB
LowC49
NVFP4
4
17.9 GB
MediumC49
Q4_K_M
4
19.5 GB
MediumC49
Q5_K_MBest for your GPU
5
23.0 GB
HighC48
Q6_K
6
26.2 GB
HighF0
Q8_0
8
34.2 GB
Very HighF0
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-Optionen

Hardware, die EXAONE 4.0 32B gut ausführt

Radeon Pro W7900 48GBGünstige Wahl
48 GB VRAM (+16)864 GB/s (+352)
C
Raises estimated decode speed by about 78%.26.1 tok/s Dekodierung

Raises estimated decode speed by about 78%.

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

ca. $3,999 MSRP

Radeon PRO W7900 DS 48GBBestes Preis-Leistungs-Verhältnis
48 GB VRAM (+16)864 GB/s (+352)
C
Raises estimated decode speed by about 78%.26.1 tok/s Dekodierung

Raises estimated decode speed by about 78%.

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

ca. $3,999 MSRP

AMD Instinct MI210 64GBAMD-Upgrade
64 GB VRAM (+32)1638 GB/s (+1126)
C
Raises estimated decode speed by about 288%.57.1 tok/s Dekodierung

Raises estimated decode speed by about 288%.

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

ca. $10,000 MSRP

NVIDIANVIDIA A100 40GBGrößter Sprung
40 GB VRAM (+8)1555 GB/s (+1043)
B
Raises estimated decode speed by about 355%.66.9 tok/s Dekodierung

Raises estimated decode speed by about 355%.

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

ca. $10,000 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run EXAONE 4.0 32B?

Yes, Radeon Pro W6800 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 14.7 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 27.4 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 Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, EXAONE 4.0 32B achieves approximately 14.7 tokens per second decode speed with a time-to-first-token of 13180ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Radeon Pro W6800 32GB receives a C grade with 14.7 tok/s and 36K context.

What context window can EXAONE 4.0 32B use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, EXAONE 4.0 32B can safely use up to 36K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GBSee 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-radeon-pro-w6800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: