Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on Intel Arc A380 6GB?

BARELY — Tight on Memory

D31Poor
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~7.2 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 7.2 GB, 9.9 tok/s, Very compromised (needs ~0.8 GB host RAM)
7.2 GB required6.0 GB available
120% VRAM needed

1.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.8 GB host RAM)

Decode

9.9 tok/s

TTFT

19619 ms

Safe context

4K

Memory

7.2 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on Intel Arc A380 6GB
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: 9.9 tok/s decode · 19.6s TTFT (warm) · 25 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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
ChatDVery compromised (needs ~0.5 GB host RAM)11.3 tok/s9316 ms4K
CodingDVery compromised (needs ~0.8 GB host RAM)9.9 tok/s19619 ms4K
Agentic CodingFToo heavy7.7 tok/s36734 ms4K
ReasoningDVery compromised (needs ~0.8 GB host RAM)9.9 tok/s23186 ms4K
RAGFToo heavy7.7 tok/s45918 ms4K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
3.0 GB
LowC54
Q3_K_S
3
3.8 GB
LowF0
NVFP4
4
4.4 GB
MediumF0
Q4_K_M
4
4.8 GB
MediumF0
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

升级选项

能流畅运行 EXAONE 3.5 7.8B Instruct 的硬件

IntelIntel Arc A580 8GB经济之选
8 GB VRAM (+2)512 GB/s (+326)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.52.7 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 432%.

~$179 MSRP

IntelIntel Arc B570 10GB最佳性价比
10 GB VRAM (+4)380 GB/s (+194)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.43.1 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 335%.

~$219 MSRP

IntelIntel Arc B580 12GBIntel 升级
12 GB VRAM (+6)456 GB/s (+270)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.46 tok/s 解码

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 365%.

~$249 MSRP

Frequently asked questions

Can Intel Arc A380 6GB run EXAONE 3.5 7.8B Instruct?

Yes, Intel Arc A380 6GB can run EXAONE 3.5 7.8B Instruct with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 9.9 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.2 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 Intel Arc A380 6GB?

On Intel Arc A380 6GB, EXAONE 3.5 7.8B Instruct achieves approximately 9.9 tokens per second decode speed with a time-to-first-token of 19619ms using Q4_K_M quantization.

Can Intel Arc A380 6GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on Intel Arc A380 6GB receives a D grade with 9.9 tok/s and 4K context.

What context window can EXAONE 3.5 7.8B Instruct use on Intel Arc A380 6GB?

On Intel Arc A380 6GB, EXAONE 3.5 7.8B Instruct can safely use up to 4K 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 Intel Arc A380 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc A380 6GB for EXAONE 3.5 7.8B Instruct?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A380 6GBSee all hardware for EXAONE 3.5 7.8B Instruct
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