Can Llama 4 Scout 17B 16E run on NVIDIA A100 80GB?

YES — With Offload

A81Great

○Estimated from fit model

Llama 4 Scout 17B 16E needs ~78.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: 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) — 78.6 GB, 65.5 tok/s, Runs with offload

78.6 GB required80.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

65.5 tok/s

TTFT

2956 ms

Safe context

24K

Memory

78.6 GB / 80.0 GB

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E on NVIDIA A100 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 65.5 tok/s decode · 3.0s TTFT (warm) · 164 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	65.5 tok/s	1612 ms	24K
Coding	A	Runs with offload	65.5 tok/s	2956 ms	24K
Agentic Coding	A	Runs with offload (needs ~1.3 GB host RAM)	54.1 tok/s	5203 ms	24K
Reasoning	A	Runs with offload	65.5 tok/s	3493 ms	24K
RAG	A	Runs with offload (needs ~1.3 GB host RAM)	54.1 tok/s	6504 ms

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	A76
Q3_K_S	3	53.4 GB	Low	A76
NVFP4Best for your GPU

Get started

Copy-paste commands to run Llama 4 Scout 17B 16E on your machine.

Run

lms load Llama-4-Scout-17B-16E-Instruct && lms server start

Your hardware

More models your NVIDIA A100 80GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	A	17.6 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	A

Frequently asked questions

See all results for NVIDIA A100 80GB See all hardware for Llama 4 Scout 17B 16E

URL: https://willitrunai.com/can-run/llama-4-scout-17b-16e-on-a100-80gb

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