Can K EXAONE 236B A23B run on AMD Instinct MI325X 256GB?

YES — Runs Great

C53Usable

Estimated from fit model

K EXAONE 236B A23B needs ~198.1 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 198.1 GB, 30.4 tok/s, Runs well

198.1 GB required256.0 GB available

77% VRAM used

Fit status

Runs well

Decode

30.4 tok/s

TTFT

6363 ms

Safe context

49K

Memory

198.1 GB / 256.0 GB

Memory breakdown

Weights144.0 GB

KV Cache27.7 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsK EXAONE 236B A23B on AMD Instinct MI325X 256GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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	30.4 tok/s	3471 ms	49K
Coding	C	Runs well	30.4 tok/s	6363 ms	49K
Agentic Coding	C	Tight fit	30.4 tok/s	9256 ms	49K
Reasoning	C	Runs well	30.4 tok/s	7520 ms	49K
RAG	C	Tight fit	30.4 tok/s	11569 ms	49K

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	C44
Q3_K_S	3	115.6 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run K EXAONE 236B A23B on your machine.

Run

lms load hf-lgai-exaone--k-exaone-236b-a23b-gguf && lms server start

Upgrade options

Hardware that runs K EXAONE 236B A23B well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+32)8000 GB/s (+2000)

Raises estimated decode speed by about 34%.40.6 tok/s decode

Raises estimated decode speed by about 34%.

~$8,000 MSRP

Frequently asked questions

Can AMD Instinct MI325X 256GB run K EXAONE 236B A23B?

Yes, AMD Instinct MI325X 256GB can run K EXAONE 236B A23B with a C grade (Runs well). Expected decode speed: 30.4 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 198.1 GB of memory with Q4_K_M quantization.

What is the best quantization for K EXAONE 236B A23B?

The recommended quantization for K EXAONE 236B A23B is Q4_K_M, which balances quality and memory efficiency.

What speed will K EXAONE 236B A23B run at on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, K EXAONE 236B A23B achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6363ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on AMD Instinct MI325X 256GB receives a C grade with 30.4 tok/s and 49K context.

What context window can K EXAONE 236B A23B use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, K EXAONE 236B A23B can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI325X 256GB See all hardware for K EXAONE 236B A23B

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