Can Llama 4 Scout 17B 16E run on Radeon Pro W7900 48GB?

YES — With Q2_K

B66Good

○Estimated from fit model

Llama 4 Scout 17B 16E needs ~51.1 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q2_K quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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.

Llama 4 Scout 17B 16E at Q4_K_M needs 75.1 GB — too much for Radeon Pro W7900 48GB (48.0 GB). Runs at Q2_K (51.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 75.1 GB, exceeds 48.0 GB available

75.1 GB required48.0 GB available

156% VRAM needed

27.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.7 tok/s

TTFT

33995 ms

Safe context

Memory

75.1 GB / 48.0 GB

Offload

40%

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 4 Scout 17B 16E on Radeon Pro W7900 48GB
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: 5.7 tok/s decode · 34.0s TTFT (warm) · 14 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	5.9 tok/s	17790 ms	4K
Coding	F	Too heavy	5.7 tok/s	33995 ms	4K
Agentic Coding	F	Too heavy	5.3 tok/s	53594 ms	4K
Reasoning	F	Too heavy	5.7 tok/s	40176 ms	4K
RAG	F	Too heavy	5.3 tok/s	66993 ms	4K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 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

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

Upgrade options

Hardware that runs Llama 4 Scout 17B 16E well

AMD Instinct MI350X 288GBBudget pick

288 GB VRAM (+240)8000 GB/s (+7136)

Makes the model fit on the accelerator instead of staying completely out of reach.223.3 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.

~$8,000 MSRP

AMD Instinct MI210 64GBAMD upgrade

64 GB VRAM (+16)1638 GB/s (+774)

Makes the model fit on the accelerator instead of staying completely out of reach.21.8 tok/s decode

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

Raises estimated decode speed by about 282%.

~$10,000 MSRP

AMD Instinct MI300A 128GBBest value

128 GB VRAM (+80)5300 GB/s (+4436)

Makes the model fit on the accelerator instead of staying completely out of reach.141.9 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

See all results for Radeon Pro W7900 48GB See all hardware for Llama 4 Scout 17B 16E

URL: https://willitrunai.com/can-run/llama-4-scout-17b-16e-on-radeon-pro-w7900-48gb

⇱

Can Llama 4 Scout 17B 16E run on Radeon Pro W7900 48GB?

YES — With Q2_K

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Llama 4 Scout 17B 16E well

Frequently asked questions