Will It Run AI

Can EXAONE 3.5 7.8B Instruct i1 run on RX 7700 XT 12GB?

YES — Runs Great

C54Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~7.8 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 7.8 GB, 54.5 tok/s, Runs well
7.8 GB required12.0 GB available
65% VRAM used

Fit status

Runs well

Decode

54.5 tok/s

TTFT

3554 ms

Safe context

90K

Memory

7.8 GB / 12.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct i1 on RX 7700 XT 12GB
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: 54.5 tok/s decode · 3.6s TTFT (warm) · 136 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 well54.5 tok/s1938 ms90K
CodingCRuns well54.5 tok/s3554 ms90K
Agentic CodingCRuns well54.5 tok/s5169 ms90K
ReasoningCRuns well54.5 tok/s4200 ms90K
RAGCRuns well54.5 tok/s6461 ms90K

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC49
Q3_K_S
3
3.8 GB
LowC50
NVFP4
4
4.4 GB
MediumC51
Q4_K_M
4
4.8 GB
MediumC51
Q5_K_M
5
5.6 GB
HighC52
Q6_K
6
6.4 GB
HighC52
Q8_0Best for your GPU
8
8.3 GB
Very HighC51
F16
16
16.0 GB
MaximumF0

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 RX 7700 XT 12GB run EXAONE 3.5 7.8B Instruct i1?

Yes, RX 7700 XT 12GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 54.5 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 7.8 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 RX 7700 XT 12GB?

On RX 7700 XT 12GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 54.5 tokens per second decode speed with a time-to-first-token of 3554ms using Q4_K_M quantization.

Can RX 7700 XT 12GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on RX 7700 XT 12GB receives a C grade with 54.5 tok/s and 90K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on RX 7700 XT 12GB?

On RX 7700 XT 12GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 90K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7700 XT 12GBSee all hardware for EXAONE 3.5 7.8B Instruct i1
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