Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on Radeon PRO W7600 8GB?

YES — Tight Fit

C51Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.4 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 7.4 GB, 35.7 tok/s, Tight fit
7.4 GB required8.0 GB available
93% VRAM used

Fit status

Tight fit

Decode

35.7 tok/s

TTFT

5421 ms

Safe context

27K

Memory

7.4 GB / 8.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on Radeon PRO W7600 8GB
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: 35.7 tok/s decode · 5.4s TTFT (warm) · 89 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit35.7 tok/s2957 ms27K
CodingCTight fit35.7 tok/s5421 ms27K
Agentic CodingCRuns with offload (needs ~0.2 GB host RAM)24.9 tok/s11321 ms27K
ReasoningCTight fit35.7 tok/s6407 ms27K
RAGCRuns with offload (needs ~0.2 GB host RAM)24.9 tok/s14151 ms27K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC54
Q3_K_S
3
3.8 GB
LowC53
NVFP4
4
4.4 GB
MediumC53
Q4_K_MBest for your GPU
4
4.8 GB
MediumC53
Q5_K_M
5
5.6 GB
HighF0
Q6_K
6
6.4 GB
HighF0
Q8_0
8
8.3 GB
Very HighF0
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

Opciones de mejora

Hardware que ejecuta bien EXAONE 3.5 7.8B Instruct

RX 7700 XT 12GBOpción económica
12 GB VRAM (+4)432 GB/s (+144)
C
Sube la velocidad estimada de decodificación alrededor de un 53%.54.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 53%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$449 MSRP

RX 6700 XT 12GBMejor relación calidad-precio
12 GB VRAM (+4)384 GB/s (+96)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$479 MSRP

RX 9070 16GBMejora AMD
16 GB VRAM (+8)640 GB/s (+352)
C
Sube la velocidad estimada de decodificación alrededor de un 134%.83.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 134%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$479 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run EXAONE 3.5 7.8B Instruct?

Yes, Radeon PRO W7600 8GB can run EXAONE 3.5 7.8B Instruct with a C grade (Tight fit). Expected decode speed: 35.7 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.4 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 W7600 8GB?

On Radeon PRO W7600 8GB, EXAONE 3.5 7.8B Instruct achieves approximately 35.7 tokens per second decode speed with a time-to-first-token of 5421ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on Radeon PRO W7600 8GB receives a C grade with 35.7 tok/s and 27K context.

What context window can EXAONE 3.5 7.8B Instruct use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, EXAONE 3.5 7.8B Instruct can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 3.5 7.8B Instruct feels slow on Radeon PRO W7600 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon PRO W7600 8GBSee all hardware for EXAONE 3.5 7.8B Instruct
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