Can EXAONE 4.0 32B run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

C49Usable

Estimated from fit model

EXAONE 4.0 32B needs ~27.4 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 27.4 GB, 19.3 tok/s, Tight fit

27.4 GB required32.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

19.3 tok/s

TTFT

10008 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 AI PRO R9700 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: 19.3 tok/s decode · 10.0s TTFT (warm) · 48 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	19.3 tok/s	5459 ms	36K
Coding	C	Tight fit	19.3 tok/s	10008 ms	36K
Agentic Coding	C	Runs with offload	19.3 tok/s	14557 ms	36K
Reasoning	C	Tight fit	19.3 tok/s	11828 ms	36K
RAG	C	Runs with offload	19.3 tok/s	18197 ms	36K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C47
Q3_K_S	3	15.7 GB	Low	C49
NVFP4	4

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

Radeon Pro W7900 48GBBudget pick

48 GB VRAM (+16)864 GB/s (+224)

Raises estimated decode speed by about 35%.26.1 tok/s decode

Raises estimated decode speed by about 35%.

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

~$3,999 MSRP

Radeon PRO W7900 DS 48GBBest value

48 GB VRAM (+16)864 GB/s (+224)

Raises estimated decode speed by about 35%.26.1 tok/s decode

Raises estimated decode speed by about 35%.

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

~$3,999 MSRP

AMD Instinct MI210 64GBAMD upgrade

64 GB VRAM (+32)1638 GB/s (+998)

Raises estimated decode speed by about 196%.57.1 tok/s decode

Raises estimated decode speed by about 196%.

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

~$10,000 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+8)1555 GB/s (+915)

Raises estimated decode speed by about 247%.66.9 tok/s decode

Raises estimated decode speed by about 247%.

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

~$10,000 MSRP

Frequently asked questions

Can Radeon AI PRO R9700 32GB run EXAONE 4.0 32B?

Yes, Radeon AI PRO R9700 32GB can run EXAONE 4.0 32B with a C grade (Tight fit). Expected decode speed: 19.3 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 AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, EXAONE 4.0 32B achieves approximately 19.3 tokens per second decode speed with a time-to-first-token of 10008ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Radeon AI PRO R9700 32GB receives a C grade with 19.3 tok/s and 36K context.

What context window can EXAONE 4.0 32B use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 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 AI PRO R9700 32GB See all hardware for EXAONE 4.0 32B

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