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URL: https://willitrunai.com/can-run/llama-4-scout-17b-16e-on-h100-pcie-80gb

⇱ Llama 4 Scout 17B 16E on NVIDIA H100 PCIe 80GB? YES

Can Llama 4 Scout 17B 16E run on NVIDIA H100 PCIe 80GB?

YES — With Offload

A81Great
○Estimated from fit model

Llama 4 Scout 17B 16E needs ~78.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Balanced
Share:

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, 64.2 tok/s, Runs with offload
78.6 GB required80.0 GB available
98% VRAM used

Fit status

Runs with offload

Decode

64.2 tok/s

TTFT

3014 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 H100 PCIe 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: 64.2 tok/s decode · 3.0s TTFT (warm) · 161 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload64.2 tok/s1644 ms24K
CodingARuns with offload64.2 tok/s3014 ms24K
Agentic CodingARuns with offload (needs ~1.3 GB host RAM)47.2 tok/s5961 ms24K
ReasoningARuns with offload64.2 tok/s3562 ms24K
RAGARuns with offload (needs ~1.3 GB host RAM)47.2 tok/s7451 ms24K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
42.5 GB
LowA76
Q3_K_S
3
53.4 GB
LowA76
NVFP4Best for your GPU
4
61.0 GB
MediumA76
Q4_K_M
4
66.5 GB
MediumF0
Q5_K_M
5
78.5 GB
HighF0
Q6_K
6
89.4 GB
HighF0
Q8_0
8
116.6 GB
Very HighF0
F16
16
223.5 GB
MaximumF0

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 H100 PCIe 80GB can run

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct		123BA14.8 tok/s
👁 Alibaba
Qwen 3.5 122B A10B		122BA44.5 tok/s
👁 Mistral
Mistral Small 4 119B		119BA47 tok/s
👁 OpenAI
GPT-OSS 120B		117BA17.1 tok/s
👁 Cohere
Command A 111B		111BS20.3 tok/s

Frequently asked questions

See all results for NVIDIA H100 PCIe 80GBSee all hardware for Llama 4 Scout 17B 16E