Can Llama 4 Scout 17B 16E run on NVIDIA GB200 192GB?

YES — Runs Great

A80Great

○Estimated from fit model

Llama 4 Scout 17B 16E needs ~89.8 GB VRAM. NVIDIA GB200 192GB has 192.0 GB. With Q4_K_M quantization, expect ~257 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 89.8 GB, 257.0 tok/s, Runs well

89.8 GB required192.0 GB available

47% VRAM used

Fit status

Runs well

Decode

257.0 tok/s

TTFT

753 ms

Safe context

574K

Memory

89.8 GB / 192.0 GB

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E on NVIDIA GB200 192GB
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: 257.0 tok/s decode · 753ms TTFT (warm) · 642 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	257.0 tok/s	411 ms	574K
Coding	A	Runs well	257.0 tok/s	753 ms	574K
Agentic Coding	A	Runs well	257.0 tok/s	1096 ms	574K
Reasoning	A	Runs well	257.0 tok/s	890 ms	574K
RAG	A	Runs well	257.0 tok/s	1370 ms	574K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA GB200 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	B68
Q3_K_S	3	53.4 GB	Low	B70
NVFP4	4	61.0 GB	Medium	A70
Q4_K_M	4	66.5 GB	Medium	A71
Q5_K_M	5	78.5 GB	High	A72
Q6_K	6	89.4 GB	High	A74
Q8_0Best for your GPU	8	116.6 GB	Very High	A76
F16	16	223.5 GB	Maximum	F0

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 GB200 192GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	97.4 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	270.2 tok/s
👁 DeepSeek DeepSeek V4 Flash	284B	S	144.8 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	292.9 tok/s
👁 OpenAI GPT-OSS 120B	117B	S	102.4 tok/s

Frequently asked questions

See all results for NVIDIA GB200 192GB See all hardware for Llama 4 Scout 17B 16E

URL: https://willitrunai.com/can-run/llama-4-scout-17b-16e-on-gb200-192gb

⇱ Llama 4 Scout 17B 16E on NVIDIA GB200 192GB? YES