Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on RX 7600 XT 16GB?

YES — Runs Great

C50Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.2 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~35 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) — 8.2 GB, 35.1 tok/s, Runs well
8.2 GB required16.0 GB available
51% VRAM used

Fit status

Runs well

Decode

35.1 tok/s

TTFT

5515 ms

Safe context

153K

Memory

8.2 GB / 16.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on RX 7600 XT 16GB
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: 35.1 tok/s decode · 5.5s TTFT (warm) · 88 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 well35.1 tok/s3008 ms153K
CodingCRuns well35.1 tok/s5515 ms153K
Agentic CodingCRuns well35.1 tok/s8021 ms153K
ReasoningCRuns well35.1 tok/s6517 ms153K
RAGCRuns well35.1 tok/s10026 ms153K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC47
Q3_K_S
3
3.8 GB
LowC47
NVFP4
4
4.4 GB
MediumC48
Q4_K_M
4
4.8 GB
MediumC48
Q5_K_M
5
5.6 GB
HighC49
Q6_K
6
6.4 GB
HighC50
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 on your machine.

Run

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

Opções de upgrade

Hardware que roda bem EXAONE 3.5 7.8B Instruct

RX 7900 XT 20GBOpção econômica
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 187%.100.9 tok/s decodificação

Raises estimated decode speed by about 187%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBMelhor custo-benefício
24 GB VRAM (+8)960 GB/s (+672)
C
Raises estimated decode speed by about 211%.109.2 tok/s decodificação

Raises estimated decode speed by about 211%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run EXAONE 3.5 7.8B Instruct?

Yes, RX 7600 XT 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 35.1 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.2 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 RX 7600 XT 16GB?

On RX 7600 XT 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5515ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on RX 7600 XT 16GB receives a C grade with 35.1 tok/s and 153K context.

What context window can EXAONE 3.5 7.8B Instruct use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, EXAONE 3.5 7.8B Instruct can safely use up to 153K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GBSee all hardware for EXAONE 3.5 7.8B Instruct
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