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	24K

Inference speed

Llama 4 Scout 17B 16E inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 4 Scout 17B 16E at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~21 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	21.3	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	17.7	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	16.8	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	13.2	Tight
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	13.1	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	8.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.5	Too big
RTX 5090 32GB	32 GB	Q4_K_M	6.9	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	6.8	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	6.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	4.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.3	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	3.8	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	3.6	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	3.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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	4	61.0 GB	Medium	A76
Q4_K_M	4	66.5 GB	Medium	F0
Q5_K_M	5	78.5 GB	High	F0
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 A100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	17.6 tok/s
Qwen 3.5 122B A10B	122B	A	52.1 tok/s
Mistral Small 4 119B	119B	A	55.3 tok/s
GPT-OSS 120B	117B	A	20 tok/s
Command A 111B	111B	S	23.2 tok/s

Frequently asked questions

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

Yes, NVIDIA A100 80GB can run Llama 4 Scout 17B 16E with a A grade (Runs with offload). Expected decode speed: 65.5 tok/s.

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

Llama 4 Scout 17B 16E (109B parameters) requires approximately 78.6 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 A100 80GB?

On NVIDIA A100 80GB, Llama 4 Scout 17B 16E achieves approximately 65.5 tokens per second decode speed with a time-to-first-token of 2956ms using Q4_K_M quantization.

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

For coding workloads, Llama 4 Scout 17B 16E on NVIDIA A100 80GB receives a A grade with 65.5 tok/s and 24K context.

What context window can Llama 4 Scout 17B 16E use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Llama 4 Scout 17B 16E can safely use up to 24K tokens of context. The model's official context limit is 10.5M, but available memory constrains the safe maximum.

What should I upgrade first if Llama 4 Scout 17B 16E feels slow on NVIDIA A100 80GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llama-4-scout-17b-16e-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: