Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on AMD Instinct MI300X 192GB?

YES — Runs Great

C45Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~25.8 GB VRAM. AMD Instinct MI300X 192GB has 192.0 GB. With Q4_K_M quantization, expect ~109 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 25.8 GB, 109.2 tok/s, Runs well
25.8 GB required192.0 GB available
13% VRAM used

Fit status

Runs well

Decode

109.2 tok/s

TTFT

1773 ms

Safe context

2.9M

Memory

25.8 GB / 192.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom19.2 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on AMD Instinct MI300X 192GB
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: 109.2 tok/s decode · 1.8s TTFT (warm) · 273 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 well109.2 tok/s967 ms2.9M
CodingCRuns well109.2 tok/s1773 ms2.9M
Agentic CodingCRuns well109.2 tok/s2579 ms2.9M
ReasoningCRuns well109.2 tok/s2095 ms2.9M
RAGCRuns well109.2 tok/s3223 ms2.9M

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on AMD Instinct MI300X 192GB (192.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowD37
Q3_K_S
3
3.8 GB
LowD37
NVFP4
4
4.4 GB
MediumD37
Q4_K_M
4
4.8 GB
MediumD37
Q5_K_M
5
5.6 GB
HighD37
Q6_K
6
6.4 GB
HighD37
Q8_0
8
8.3 GB
Very HighD37
F16Best for your GPU
16
16.0 GB
MaximumD37

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.

Run

lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI300X 192GB run EXAONE 3.5 7.8B Instruct?

Yes, AMD Instinct MI300X 192GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 109.2 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 25.8 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct?

The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct run at on AMD Instinct MI300X 192GB?

On AMD Instinct MI300X 192GB, EXAONE 3.5 7.8B Instruct achieves approximately 109.2 tokens per second decode speed with a time-to-first-token of 1773ms using Q4_K_M quantization.

Can AMD Instinct MI300X 192GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on AMD Instinct MI300X 192GB receives a C grade with 109.2 tok/s and 2.9M context.

What context window can EXAONE 3.5 7.8B Instruct use on AMD Instinct MI300X 192GB?

On AMD Instinct MI300X 192GB, EXAONE 3.5 7.8B Instruct can safely use up to 2.9M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI300X 192GBSee all hardware for EXAONE 3.5 7.8B Instruct
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