Will It Run AI

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
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) — 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well32.8 tok/s3221 ms148K
CodingCRuns well32.8 tok/s5905 ms148K
Agentic CodingCRuns well32.8 tok/s8589 ms148K
ReasoningCRuns well32.8 tok/s6978 ms148K
RAGCRuns well32.8 tok/s10736 ms148K

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).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC47
Q3_K_S
3
3.8 GB
LowC47
NVFP4
4
4.4 GB
MediumC48
Q4_K_M
4
4.8 GB
MediumC48
Q5_K_M
5
5.6 GB
HighC49
Q6_K
6
6.4 GB
HighC50
Q8_0Best for your GPU
8
8.3 GB
Very HighC51
F16
16
16.0 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien EXAONE 3.5 7.8B Instruct i1

NVIDIARTX 4000 Ada 20GBOpción económica
20 GB VRAM (+4)360 GB/s (+160)
C
Sube la velocidad estimada de decodificación alrededor de un 80%.59 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 80%.

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

~$1,250 MSRP

NVIDIARTX 3090 24GBMejor relación calidad-precio
24 GB VRAM (+8)936 GB/s (+736)
C
Sube la velocidad estimada de decodificación alrededor de un 233%.109.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 233%.

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

~$1,499 MSRP

NVIDIARTX PRO 4000 Blackwell 24GBMejora NVIDIA
24 GB VRAM (+8)672 GB/s (+472)
C
Sube la velocidad estimada de decodificación alrededor de un 233%.109.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 233%.

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

~$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 16GBSee all hardware for EXAONE 3.5 7.8B Instruct i1
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