Can EXAONE 3.5 7.8B Instruct i1 run on NVIDIA A2 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~8.5 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 8.5 GB, 32.8 tok/s, Runs well

8.5 GB required16.0 GB available

53% VRAM used

Fit status

Runs well

Decode

32.8 tok/s

TTFT

5905 ms

Safe context

148K

Memory

8.5 GB / 16.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct i1 on NVIDIA A2 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: 32.8 tok/s decode · 5.9s TTFT (warm) · 82 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	32.8 tok/s	3221 ms	148K
Coding	C	Runs well	32.8 tok/s	5905 ms	148K
Agentic Coding	C	Runs well	32.8 tok/s	8589 ms	148K
Reasoning	C	Runs well	32.8 tok/s	6978 ms	148K
RAG	C	Runs well	32.8 tok/s	10736 ms	148K

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on NVIDIA A2 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

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.59 tok/s decode

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 233%.109.2 tok/s decode

Raises estimated decode speed by about 233%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 233%.109.2 tok/s decode

Raises estimated decode speed by about 233%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run EXAONE 3.5 7.8B Instruct i1?

Yes, NVIDIA A2 16GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 32.8 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 8.5 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on NVIDIA A2 16GB receives a C grade with 32.8 tok/s and 148K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 148K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for EXAONE 3.5 7.8B Instruct i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: