Can Llama 4 Scout 17B 16E run on RTX 4000 Ada 20GB?

Q: What should I upgrade first if Llama 4 Scout 17B 16E feels slow on RTX 4000 Ada 20GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

Llama 4 Scout 17B 16E needs ~72.6 GB but RTX 4000 Ada 20GB only has 20.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity

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) — 72.6 GB, exceeds 20.0 GB available

72.6 GB required20.0 GB available

363% VRAM needed

52.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

72.6 GB / 20.0 GB

Offload

70%

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 4 Scout 17B 16E on RTX 4000 Ada 20GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 72.6 GB, but this setup only exposes 20.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	42.5 GB	Low	F0
Q3_K_S	3	53.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Llama 4 Scout 17B 16E well

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+76)1792 GB/s (+1432)

Makes the model fit on the accelerator instead of staying completely out of reach.57.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+76)1597 GB/s (+1237)

Makes the model fit on the accelerator instead of staying completely out of reach.51.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+76)4000 GB/s (+3640)

Makes the model fit on the accelerator instead of staying completely out of reach.123.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$12,000 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run Llama 4 Scout 17B 16E?

No, Llama 4 Scout 17B 16E requires more memory than RTX 4000 Ada 20GB provides.

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

Llama 4 Scout 17B 16E (109B parameters) requires approximately 72.6 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 RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Llama 4 Scout 17B 16E achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Llama 4 Scout 17B 16E for coding?

For coding workloads, Llama 4 Scout 17B 16E on RTX 4000 Ada 20GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Llama 4 Scout 17B 16E use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Llama 4 Scout 17B 16E can safely use up to 4K 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 RTX 4000 Ada 20GB?

See all results for RTX 4000 Ada 20GB 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-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: