Can EXAONE 3.5 7.8B Instruct i1 run on RX 9060 XT 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~8.2 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 8.2 GB, 42.4 tok/s, Runs well

8.2 GB required16.0 GB available

51% VRAM used

Fit status

Runs well

Decode

42.4 tok/s

TTFT

4569 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 i1 on RX 9060 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: 42.4 tok/s decode · 4.6s TTFT (warm) · 106 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	42.4 tok/s	2492 ms	153K
Coding	C	Runs well	42.4 tok/s	4569 ms	153K
Agentic Coding	C	Runs well	42.4 tok/s	6646 ms	153K
Reasoning	C	Runs well	42.4 tok/s	5400 ms	153K
RAG	C	Runs well	42.4 tok/s	8308 ms	153K

Inference speed

EXAONE 3.5 7.8B Instruct i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 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?
RTX 5090 32GB	32 GB	Q4_K_M	109.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	109.2

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C47
Q3_K_S	3	3.8 GB	Low	C47
NVFP4	4

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct i1 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 138%.100.9 tok/s decode

Raises estimated decode speed by about 138%.

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

~$899 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 147%.104.9 tok/s decode

Raises estimated decode speed by about 147%.

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

~$2,000 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run EXAONE 3.5 7.8B Instruct i1?

Yes, RX 9060 XT 16GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 42.4 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 8.2 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 9060 XT 16GB?

On RX 9060 XT 16GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 42.4 tokens per second decode speed with a time-to-first-token of 4569ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on RX 9060 XT 16GB receives a C grade with 42.4 tok/s and 153K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, EXAONE 3.5 7.8B Instruct i1 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 9060 XT 16GB See all hardware for EXAONE 3.5 7.8B Instruct i1

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