Will It Run AI

Can EXAONE 3.5 7.8B Instruct i1 run on AMD Instinct MI350X 288GB?

YES — Runs Great

C44Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~35.4 GB VRAM. AMD Instinct MI350X 288GB has 288.0 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) — 35.4 GB, 109.2 tok/s, Runs well
35.4 GB required288.0 GB available
12% VRAM used

Fit status

Runs well

Decode

109.2 tok/s

TTFT

1773 ms

Safe context

4.4M

Memory

35.4 GB / 288.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom28.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct i1 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: 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 ms4.4M
CodingCRuns well109.2 tok/s1773 ms4.4M
Agentic CodingCRuns well109.2 tok/s2579 ms4.4M
ReasoningCRuns well109.2 tok/s2095 ms4.4M
RAGCRuns well109.2 tok/s3223 ms4.4M

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowD36
Q3_K_S
3
3.8 GB
LowD36
NVFP4
4
4.4 GB
MediumD36
Q4_K_M
4
4.8 GB
MediumD36
Q5_K_M
5
5.6 GB
HighD36
Q6_K
6
6.4 GB
HighD36
Q8_0
8
8.3 GB
Very HighD36
F16Best for your GPU
16
16.0 GB
MaximumD36

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.

Run

lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI350X 288GB run EXAONE 3.5 7.8B Instruct i1?

Yes, AMD Instinct MI350X 288GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 109.2 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?

EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 35.4 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct i1?

The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct i1 run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, EXAONE 3.5 7.8B Instruct i1 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 MI350X 288GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on AMD Instinct MI350X 288GB receives a C grade with 109.2 tok/s and 4.4M context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 4.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GBSee all hardware for EXAONE 3.5 7.8B Instruct i1
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