Can EXAONE 3.5 7.8B Instruct run on Tesla P100 16GB?

YES — Runs Great

C53Usable

○Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.5 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~91 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) — 8.5 GB, 90.8 tok/s, Runs well

8.5 GB required16.0 GB available

53% VRAM used

Fit status

Runs well

Decode

90.8 tok/s

TTFT

2133 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 Tesla P100 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: 90.8 tok/s decode · 2.1s TTFT (warm) · 227 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	90.8 tok/s	1163 ms	148K
Coding	C	Runs well	90.8 tok/s	2133 ms	148K
Agentic Coding	C	Runs well	90.8 tok/s	3102 ms	148K
Reasoning	C	Runs well	90.8 tok/s	2521 ms	148K
RAG	C	Runs well	90.8 tok/s	3878 ms	148K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Tesla P100 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

Frequently asked questions

See all results for Tesla P100 16GB See all hardware for EXAONE 3.5 7.8B Instruct

URL: https://willitrunai.com/can-run/hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf-on-tesla-p100-16gb

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