Can K EXAONE 236B A23B run on AMD Instinct MI350X 288GB?

YES — Runs Great

C54Usable

Estimated from fit model

K EXAONE 236B A23B needs ~201.3 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~41 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) — 201.3 GB, 40.6 tok/s, Runs well

201.3 GB required288.0 GB available

70% VRAM used

Fit status

Runs well

Decode

40.6 tok/s

TTFT

4772 ms

Safe context

66K

Memory

201.3 GB / 288.0 GB

Memory breakdown

Weights144.0 GB

KV Cache27.7 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsK EXAONE 236B A23B on AMD Instinct MI350X 288GB
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: 40.6 tok/s decode · 4.8s TTFT (warm) · 101 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	40.6 tok/s	2603 ms	66K
Coding	C	Runs well	40.6 tok/s	4772 ms	66K
Agentic Coding	C	Runs well	40.6 tok/s	6942 ms	66K
Reasoning	C	Runs well	40.6 tok/s	5640 ms	66K
RAG	C	Runs well	40.6 tok/s	8677 ms	66K

Inference speed

K EXAONE 236B A23B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for K EXAONE 236B A23B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~3 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	3.4	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.0	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 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 K EXAONE 236B A23B (236B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	C43
Q3_K_S	3	115.6 GB	Low	C44
NVFP4	4	132.2 GB	Medium	C46
Q4_K_M	4	144.0 GB	Medium	C47
Q5_K_M	5	169.9 GB	High	C48
Q6_KBest for your GPU	6	193.5 GB	High	C48
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

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

Frequently asked questions

Can AMD Instinct MI350X 288GB run K EXAONE 236B A23B?

Yes, AMD Instinct MI350X 288GB can run K EXAONE 236B A23B with a C grade (Runs well). Expected decode speed: 40.6 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 201.3 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 MI350X 288GB?

On AMD Instinct MI350X 288GB, K EXAONE 236B A23B achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4772ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on AMD Instinct MI350X 288GB receives a C grade with 40.6 tok/s and 66K context.

What context window can K EXAONE 236B A23B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, K EXAONE 236B A23B can safely use up to 66K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for K EXAONE 236B A23B

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<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--k-exaone-236b-a23b-gguf-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

40.6 tok/s

2603 ms

66K

Coding

Runs well

40.6 tok/s

4772 ms

66K

Agentic Coding

Runs well

40.6 tok/s

6942 ms

66K

Reasoning

Runs well

40.6 tok/s

5640 ms

66K

RAG

Runs well

40.6 tok/s

8677 ms

66K

K EXAONE 236B A23B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

3.4

Heavy offload

RTX 5090 32GB

32 GB

Q4_K_M

2.0

Too big

RTX 4090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

2.0

Too big

RTX 3090 24GB

24 GB

Q4_K_M

2.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.0

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

RX 7900 XTX 24GB

24 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

2.0

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

2.0

Too big

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

2.0

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

2.0

Too big

Quantization options

How K EXAONE 236B A23B (236B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	92.0 GB	Low	C43
Q3_K_S	3	115.6 GB	Low	C44
NVFP4	4	132.2 GB	Medium	C46
Q4_K_M	4	144.0 GB	Medium	C47
Q5_K_M	5	169.9 GB	High	C48
Q6_KBest for your GPU	6	193.5 GB	High	C48
Q8_0	8	252.5 GB	Very High	F0
F16	16	483.8 GB	Maximum	F0

Frequently asked questions

Can AMD Instinct MI350X 288GB run K EXAONE 236B A23B?

Yes, AMD Instinct MI350X 288GB can run K EXAONE 236B A23B with a C grade (Runs well). Expected decode speed: 40.6 tok/s.

How much VRAM does K EXAONE 236B A23B need?

K EXAONE 236B A23B (236B parameters) requires approximately 201.3 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 MI350X 288GB?

On AMD Instinct MI350X 288GB, K EXAONE 236B A23B achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4772ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run K EXAONE 236B A23B for coding?

For coding workloads, K EXAONE 236B A23B on AMD Instinct MI350X 288GB receives a C grade with 40.6 tok/s and 66K context.

What context window can K EXAONE 236B A23B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, K EXAONE 236B A23B can safely use up to 66K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.