Can Llama 4 Scout 17B 16E run on NVIDIA H20 96GB?

YES — Tight Fit

A82Great

Estimated from fit model

Llama 4 Scout 17B 16E needs ~80.2 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~115 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 80.2 GB, 123.9 tok/s, Tight fit

80.2 GB required96.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

123.9 tok/s

TTFT

1563 ms

Safe context

102K

Memory

80.2 GB / 96.0 GB

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E on NVIDIA H20 96GB
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: 123.9 tok/s decode · 1.6s TTFT (warm) · 310 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	Tight fit	123.9 tok/s	852 ms	102K
Coding	A	Tight fit	114.7 tok/s	1688 ms	102K
Agentic Coding	A	Tight fit	123.9 tok/s	2273 ms	102K
Reasoning	A	Tight fit	123.9 tok/s	1847 ms	102K
RAG	A	Tight fit	123.9 tok/s	2841 ms	102K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	A74
Q3_K_S	3	53.4 GB	Low	A76
NVFP4	4	61.0 GB	Medium	A76
Q4_K_M	4	66.5 GB	Medium	A76
Q5_K_MBest for your GPU	5	78.5 GB	High	A76
Q6_K	6	89.4 GB	High	F0
Q8_0	8	116.6 GB	Very High	F0
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 H20 96GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	47 tok/s
Qwen 3.5 122B A10B	122B	S	130.3 tok/s
Mistral Small 4 119B	119B	S	141.2 tok/s
GPT-OSS 120B	117B	S	49.4 tok/s
Command A 111B	111B	S	52.2 tok/s

Frequently asked questions

Can NVIDIA H20 96GB run Llama 4 Scout 17B 16E?

Yes, NVIDIA H20 96GB can run Llama 4 Scout 17B 16E with a A grade (Tight fit). Expected decode speed: 114.7 tok/s.

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

Llama 4 Scout 17B 16E (109B parameters) requires approximately 80.2 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 H20 96GB?

On NVIDIA H20 96GB, Llama 4 Scout 17B 16E achieves approximately 114.7 tokens per second decode speed with a time-to-first-token of 1688ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Llama 4 Scout 17B 16E for coding?

For coding workloads, Llama 4 Scout 17B 16E on NVIDIA H20 96GB receives a A grade with 114.7 tok/s and 102K context.

What context window can Llama 4 Scout 17B 16E use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Llama 4 Scout 17B 16E can safely use up to 102K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Llama 4 Scout 17B 16E

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