Can EXAONE 3.5 7.8B Instruct run on NVIDIA T4 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.5 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.5 GB, 43.7 tok/s, Runs well

8.5 GB required16.0 GB available

53% VRAM used

Fit status

Runs well

Decode

43.7 tok/s

TTFT

4429 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 on NVIDIA T4 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: 43.7 tok/s decode · 4.4s TTFT (warm) · 109 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	43.7 tok/s	2416 ms	148K
Coding	C	Runs well	43.7 tok/s	4429 ms	148K
Agentic Coding	C	Runs well	43.7 tok/s	6442 ms	148K
Reasoning	C	Runs well	43.7 tok/s	5234 ms	148K
RAG	C	Runs well	43.7 tok/s	8052 ms	148K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on NVIDIA T4 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 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

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 131%.100.9 tok/s decode

Raises estimated decode speed by about 131%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 140%.104.9 tok/s decode

Raises estimated decode speed by about 140%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run EXAONE 3.5 7.8B Instruct?

Yes, NVIDIA T4 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 43.7 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (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?

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 T4 16GB?

On NVIDIA T4 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 43.7 tokens per second decode speed with a time-to-first-token of 4429ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on NVIDIA T4 16GB receives a C grade with 43.7 tok/s and 148K context.

What context window can EXAONE 3.5 7.8B Instruct use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, EXAONE 3.5 7.8B Instruct 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 T4 16GB See all hardware for EXAONE 3.5 7.8B Instruct

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