Can EXAONE 3.5 7.8B Instruct run on NVIDIA A16 64GB?

YES — Runs Great

C46Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~13.3 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 13.3 GB, 98.4 tok/s, Runs well

13.3 GB required64.0 GB available

21% VRAM used

Fit status

Runs well

Decode

98.4 tok/s

TTFT

1968 ms

Safe context

904K

Memory

13.3 GB / 64.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on NVIDIA A16 64GB
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: 98.4 tok/s decode · 2.0s TTFT (warm) · 246 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	98.4 tok/s	1074 ms	904K
Coding	C	Runs well	98.4 tok/s	1968 ms	904K
Agentic Coding	C	Runs well	98.4 tok/s	2863 ms	904K
Reasoning	C	Runs well	98.4 tok/s	2326 ms	904K
RAG	C	Runs well	98.4 tok/s	3579 ms	904K

Inference speed

EXAONE 3.5 7.8B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct 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

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C40
Q3_K_S	3	3.8 GB	Low	C40
NVFP4	4

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

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.78.8 tok/s decode

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

This setup is broadly balanced for this model.109.2 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run EXAONE 3.5 7.8B Instruct?

Yes, NVIDIA A16 64GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 98.4 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 13.3 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, EXAONE 3.5 7.8B Instruct achieves approximately 98.4 tokens per second decode speed with a time-to-first-token of 1968ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on NVIDIA A16 64GB receives a C grade with 98.4 tok/s and 904K context.

What context window can EXAONE 3.5 7.8B Instruct use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, EXAONE 3.5 7.8B Instruct can safely use up to 904K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for EXAONE 3.5 7.8B Instruct

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