Will It Run AI

Can Llama 4 Maverick 17B 128E run on AMD Instinct MI325X 256GB?

YES — With Offload

A75Great
Estimated from fit model

Llama 4 Maverick 17B 128E needs ~273.4 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 273.4 GB, 37.9 tok/s, Runs with offload (needs ~15.6 GB host RAM)
273.4 GB required256.0 GB available
107% VRAM needed

17.4 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~15.6 GB host RAM)

Decode

37.9 tok/s

TTFT

5109 ms

Safe context

4K

Memory

273.4 GB / 256.0 GB

Offload

10%

Memory breakdown

Weights244.0 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom25.6 GB

See how fast it feels

See how fast it feelsLlama 4 Maverick 17B 128E on AMD Instinct MI325X 256GB
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: 37.9 tok/s decode · 5.1s TTFT (warm) · 95 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 15.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~14.3 GB host RAM)38.3 tok/s2755 ms4K
CodingARuns with offload (needs ~15.6 GB host RAM)37.9 tok/s5109 ms4K
Agentic CodingARuns with offload (needs ~18 GB host RAM)37.1 tok/s7599 ms4K
ReasoningARuns with offload (needs ~15.6 GB host RAM)37.9 tok/s6037 ms4K
RAGARuns with offload (needs ~18 GB host RAM)37.1 tok/s9499 ms4K

Quantization options

How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
156.0 GB
LowA82
Q3_K_SBest for your GPU
3
196.0 GB
LowA82
NVFP4
4
224.0 GB
MediumF0
Q4_K_M
4
244.0 GB
MediumF0
Q5_K_M
5
288.0 GB
HighF0
Q6_K
6
328.0 GB
HighF0
Q8_0
8
428.0 GB
Very HighF0
F16
16
820.0 GB
MaximumF0

Get started

Copy-paste commands to run Llama 4 Maverick 17B 128E on your machine.

Run

lms load Llama-4-Maverick-17B-128E-Instruct && lms server start

Frequently asked questions

Can AMD Instinct MI325X 256GB run Llama 4 Maverick 17B 128E?

Yes, AMD Instinct MI325X 256GB can run Llama 4 Maverick 17B 128E with a A grade (Runs with offload (needs ~15.6 GB host RAM)). Expected decode speed: 37.9 tok/s.

How much VRAM does Llama 4 Maverick 17B 128E need?

Llama 4 Maverick 17B 128E (400B parameters) requires approximately 273.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 4 Maverick 17B 128E?

The recommended quantization for Llama 4 Maverick 17B 128E is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 4 Maverick 17B 128E run at on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Llama 4 Maverick 17B 128E achieves approximately 37.9 tokens per second decode speed with a time-to-first-token of 5109ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run Llama 4 Maverick 17B 128E for coding?

For coding workloads, Llama 4 Maverick 17B 128E on AMD Instinct MI325X 256GB receives a A grade with 37.9 tok/s and 4K context.

What context window can Llama 4 Maverick 17B 128E use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Llama 4 Maverick 17B 128E feels slow on AMD Instinct MI325X 256GB?

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.

See all results for AMD Instinct MI325X 256GBSee all hardware for Llama 4 Maverick 17B 128E
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