Can Llama 4 Scout 17B 16E run on NVIDIA B200 180GB?

YES — Runs Great

A80Great

Estimated from fit model

Llama 4 Scout 17B 16E needs ~88.6 GB VRAM. NVIDIA B200 180GB has 180.0 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) — 88.6 GB, 257.0 tok/s, Runs well

88.6 GB required180.0 GB available

49% VRAM used

Fit status

Runs well

Decode

257.0 tok/s

TTFT

753 ms

Safe context

515K

Memory

88.6 GB / 180.0 GB

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E on NVIDIA B200 180GB
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	515K
Coding	A	Runs well	257.0 tok/s	753 ms	515K
Agentic Coding	A	Runs well	257.0 tok/s	1096 ms	515K
Reasoning	A	Runs well	257.0 tok/s	890 ms	515K
RAG	A	Runs well	257.0 tok/s	1370 ms	515K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	B69
Q3_K_S	3	53.4 GB	Low	A70
NVFP4	4	61.0 GB	Medium	A71
Q4_K_M	4	66.5 GB	Medium	A72
Q5_K_M	5	78.5 GB	High	A73
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 B200 180GB can run

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

Frequently asked questions

Can NVIDIA B200 180GB run Llama 4 Scout 17B 16E?

Yes, NVIDIA B200 180GB can run Llama 4 Scout 17B 16E with a A grade (Runs well). Expected decode speed: 257.0 tok/s.

How much VRAM does Llama 4 Scout 17B 16E need?

Llama 4 Scout 17B 16E (109B parameters) requires approximately 88.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 4 Scout 17B 16E?

The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 4 Scout 17B 16E run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 4 Scout 17B 16E achieves approximately 257.0 tokens per second decode speed with a time-to-first-token of 753ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Llama 4 Scout 17B 16E for coding?

For coding workloads, Llama 4 Scout 17B 16E on NVIDIA B200 180GB receives a A grade with 257.0 tok/s and 515K context.

What context window can Llama 4 Scout 17B 16E use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 4 Scout 17B 16E can safely use up to 515K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.

See all results for NVIDIA B200 180GB See all hardware for Llama 4 Scout 17B 16E

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

257.0 tok/s

411 ms

515K

Coding

Runs well

257.0 tok/s

753 ms

515K

Agentic Coding

Runs well

257.0 tok/s

1096 ms

515K

Reasoning

Runs well

257.0 tok/s

890 ms

515K

RAG

Runs well

257.0 tok/s

1370 ms

515K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	B69
Q3_K_S	3	53.4 GB	Low	A70
NVFP4	4	61.0 GB	Medium	A71
Q4_K_M	4	66.5 GB	Medium	A72
Q5_K_M	5	78.5 GB	High	A73
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

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

97.4 tok/s

Qwen 3.5 122B A10B

122B

270.2 tok/s

DeepSeek V4 Flash

284B

144.8 tok/s

Mistral Small 4 119B

119B

292.9 tok/s

GPT-OSS 120B

117B

102.4 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Llama 4 Scout 17B 16E?

Yes, NVIDIA B200 180GB can run Llama 4 Scout 17B 16E with a A grade (Runs well). Expected decode speed: 257.0 tok/s.

How much VRAM does Llama 4 Scout 17B 16E need?

Llama 4 Scout 17B 16E (109B parameters) requires approximately 88.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 4 Scout 17B 16E?

The recommended quantization for Llama 4 Scout 17B 16E is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 4 Scout 17B 16E run at on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 4 Scout 17B 16E achieves approximately 257.0 tokens per second decode speed with a time-to-first-token of 753ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Llama 4 Scout 17B 16E for coding?

For coding workloads, Llama 4 Scout 17B 16E on NVIDIA B200 180GB receives a A grade with 257.0 tok/s and 515K context.

What context window can Llama 4 Scout 17B 16E use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Llama 4 Scout 17B 16E can safely use up to 515K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.