Can exaone 3.0 7.8b it run on Tesla P40 24GB?

YES — Runs Great

C47Usable

Estimated from fit model

exaone 3.0 7.8b it needs ~9.3 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 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) — 9.3 GB, 42.9 tok/s, Runs well

9.3 GB required24.0 GB available

39% VRAM used

Fit status

Runs well

Decode

42.9 tok/s

TTFT

4512 ms

Safe context

274K

Memory

9.3 GB / 24.0 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it on Tesla P40 24GB
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: 42.9 tok/s decode · 4.5s TTFT (warm) · 107 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	42.9 tok/s	2461 ms	274K
Coding	C	Runs well	42.9 tok/s	4512 ms	274K
Agentic Coding	C	Runs well	42.9 tok/s	6563 ms	274K
Reasoning	C	Runs well	42.9 tok/s	5333 ms	274K
RAG	C	Runs well	42.9 tok/s	8204 ms	274K

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C44
Q3_K_S	3	3.8 GB	Low	C45
NVFP4	4	4.4 GB	Medium	C45
Q4_K_M	4	4.8 GB	Medium	C45
Q5_K_M	5	5.6 GB	High	C45
Q6_K	6	6.4 GB	High	C46
Q8_0	8	8.3 GB	Very High	C47
F16Best for your GPU	16	16.0 GB	Maximum	C49

Get started

Copy-paste commands to run exaone 3.0 7.8b it on your machine.

Run

lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start

Upgrade-Optionen

Hardware, die exaone 3.0 7.8b it gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 155%.109.2 tok/s Dekodierung

Raises estimated decode speed by about 155%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 155%.109.2 tok/s Dekodierung

Raises estimated decode speed by about 155%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.96.8 tok/s Dekodierung

Raises estimated decode speed by about 126%.

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

ca. $4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run exaone 3.0 7.8b it?

Yes, Tesla P40 24GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 42.9 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for exaone 3.0 7.8b it?

The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.

What speed will exaone 3.0 7.8b it run at on Tesla P40 24GB?

On Tesla P40 24GB, exaone 3.0 7.8b it achieves approximately 42.9 tokens per second decode speed with a time-to-first-token of 4512ms using Q4_K_M quantization.

Can Tesla P40 24GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on Tesla P40 24GB receives a C grade with 42.9 tok/s and 274K context.

What context window can exaone 3.0 7.8b it use on Tesla P40 24GB?

On Tesla P40 24GB, exaone 3.0 7.8b it can safely use up to 274K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for exaone 3.0 7.8b it

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