Can EXAONE 3.5 7.8B Instruct i1 run on Intel Arc A580 8GB?

YES — Tight Fit

C52Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~7.4 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 7.4 GB, 52.7 tok/s, Tight fit

7.4 GB required8.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

52.7 tok/s

TTFT

3672 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 i1 on Intel Arc A580 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: 52.7 tok/s decode · 3.7s TTFT (warm) · 132 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	52.7 tok/s	2003 ms	27K
Coding	C	Tight fit	52.7 tok/s	3672 ms	27K
Agentic Coding	C	Runs with offload (needs ~0.2 GB host RAM)	36.7 tok/s	7668 ms	27K
Reasoning	C	Tight fit	52.7 tok/s	4339 ms	27K
RAG	C	Runs with offload (needs ~0.2 GB host RAM)	36.7 tok/s	9584 ms	27K

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	Fits
RTX 3090 24GB	24 GB	Q4_K_M	109.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	109.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	97.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	92.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	79.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	78.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	78.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	50.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	49.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	46.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	41.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C54
Q3_K_S	3	3.8 GB	Low	C53
NVFP4	4	4.4 GB	Medium	C53
Q4_K_MBest for your GPU	4	4.8 GB	Medium	C53
Q5_K_M	5	5.6 GB	High	F0
Q6_K	6	6.4 GB	High	F0
Q8_0	8	8.3 GB	Very High	F0
F16	16	16.0 GB	Maximum	F0

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

アップグレードオプション

EXAONE 3.5 7.8B Instruct i1を快適に動かすハードウェア

Intel Arc B570 10GBコスパ重視

10 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.43.1 tok/sデコード

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

〜$219 MSRP

Intel Arc B580 12GBコスパ最良

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.46 tok/sデコード

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

〜$249 MSRP

Intel Arc Pro A60 12GBIntelアップグレード

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.39.5 tok/sデコード

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

〜$499 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run EXAONE 3.5 7.8B Instruct i1?

Yes, Intel Arc A580 8GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Tight fit). Expected decode speed: 52.7 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 7.4 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 Intel Arc A580 8GB?

On Intel Arc A580 8GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 52.7 tokens per second decode speed with a time-to-first-token of 3672ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on Intel Arc A580 8GB receives a C grade with 52.7 tok/s and 27K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, EXAONE 3.5 7.8B Instruct i1 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 i1 feels slow on Intel Arc A580 8GB?

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.

Would CUDA be a better path than Intel Arc A580 8GB for EXAONE 3.5 7.8B Instruct i1?

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 A580 8GB See all hardware for EXAONE 3.5 7.8B Instruct i1

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