Will It Run AI

Can EXAONE 3.5 7.8B Instruct run on Quadro RTX 8000 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~11.7 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 11.7 GB, 97.5 tok/s, Runs well
11.7 GB required48.0 GB available
24% VRAM used

Fit status

Runs well

Decode

97.5 tok/s

TTFT

1987 ms

Safe context

652K

Memory

11.7 GB / 48.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on Quadro RTX 8000 48GB
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: 97.5 tok/s decode · 2.0s TTFT (warm) · 244 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well97.5 tok/s1084 ms652K
CodingCRuns well97.5 tok/s1987 ms652K
Agentic CodingCRuns well97.5 tok/s2890 ms652K
ReasoningCRuns well97.5 tok/s2348 ms652K
RAGCRuns well97.5 tok/s3612 ms652K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC41
Q3_K_S
3
3.8 GB
LowC41
NVFP4
4
4.4 GB
MediumC41
Q4_K_M
4
4.8 GB
MediumC41
Q5_K_M
5
5.6 GB
HighC42
Q6_K
6
6.4 GB
HighC42
Q8_0
8
8.3 GB
Very HighC42
F16Best for your GPU
16
16.0 GB
MaximumC44

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-Optionen

Hardware, die EXAONE 3.5 7.8B Instruct gut ausführt

Mac Studio M3 Ultra 96GBGünstige Wahl
96 GB Unified (+48)819 GB/s (+147)
C
Adds memory headroom for longer context windows and future model growth.109.2 tok/s Dekodierung

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

ca. $3,999 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run EXAONE 3.5 7.8B Instruct?

Yes, Quadro RTX 8000 48GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 97.5 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 11.7 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, EXAONE 3.5 7.8B Instruct achieves approximately 97.5 tokens per second decode speed with a time-to-first-token of 1987ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on Quadro RTX 8000 48GB receives a C grade with 97.5 tok/s and 652K context.

What context window can EXAONE 3.5 7.8B Instruct use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, EXAONE 3.5 7.8B Instruct can safely use up to 652K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GBSee all hardware for EXAONE 3.5 7.8B Instruct
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