Can EXAONE 3.5 7.8B Instruct run on Radeon Pro W7900 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~11.4 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~107 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 11.4 GB, 107.1 tok/s, Runs well

11.4 GB required48.0 GB available

24% VRAM used

Fit status

Runs well

Decode

107.1 tok/s

TTFT

1807 ms

Safe context

657K

Memory

11.4 GB / 48.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on Radeon Pro W7900 48GB
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: 107.1 tok/s decode · 1.8s TTFT (warm) · 268 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	107.1 tok/s	986 ms	657K
Coding	C	Runs well	107.1 tok/s	1807 ms	657K
Agentic Coding	C	Runs well	107.1 tok/s	2628 ms	657K
Reasoning	C	Runs well	107.1 tok/s	2136 ms	657K
RAG	C	Runs well	107.1 tok/s	3285 ms	657K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C41
Q3_K_S	3	3.8 GB	Low	C41
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.109.2 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run EXAONE 3.5 7.8B Instruct?

Yes, Radeon Pro W7900 48GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 107.1 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 11.4 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, EXAONE 3.5 7.8B Instruct achieves approximately 107.1 tokens per second decode speed with a time-to-first-token of 1807ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on Radeon Pro W7900 48GB receives a C grade with 107.1 tok/s and 657K context.

What context window can EXAONE 3.5 7.8B Instruct use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, EXAONE 3.5 7.8B Instruct can safely use up to 657K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for EXAONE 3.5 7.8B Instruct

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