Can Llama 4 Scout 17B 16E run on AMD Instinct MI210 64GB?

BARELY — Tight on Memory

B61Good

○Estimated from fit model

Llama 4 Scout 17B 16E needs ~76.7 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~22 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 76.7 GB, 21.8 tok/s, Very compromised (needs ~11 GB host RAM)

76.7 GB required64.0 GB available

120% VRAM needed

12.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~11 GB host RAM)

Decode

21.8 tok/s

TTFT

8876 ms

Safe context

Memory

76.7 GB / 64.0 GB

Offload

20%

Memory breakdown

Weights66.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsLlama 4 Scout 17B 16E on AMD Instinct MI210 64GB
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: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 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 20% 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 11.0 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	B	Very compromised (needs ~9.9 GB host RAM)	22.7 tok/s	4649 ms	4K
Coding	B	Very compromised (needs ~11 GB host RAM)	21.8 tok/s	8876 ms	4K
Agentic Coding	F	Too heavy	20.2 tok/s	13971 ms	4K
Reasoning	B	Very compromised (needs ~11 GB host RAM)	21.8 tok/s	10490 ms	4K
RAG	F	Too heavy	20.2 tok/s	17463 ms	4K

Quantization options

How Llama 4 Scout 17B 16E (109B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	42.5 GB	Low	A76
Q3_K_S	3	53.4 GB	Low	F0
NVFP4	4	61.0 GB	Medium	F0
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