Can Llama 3.1 405B run on AMD Instinct MI325X 256GB?

Q: What should I upgrade first if Llama 3.1 405B 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.

BARELY — Tight on Memory

A71Great

Estimated from fit model

Llama 3.1 405B needs ~281.2 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~12 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) — 281.2 GB, 11.9 tok/s, Very compromised (needs ~22.2 GB host RAM)

281.2 GB required256.0 GB available

110% VRAM needed

25.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~22.2 GB host RAM)

Decode

11.9 tok/s

TTFT

16226 ms

Safe context

Memory

281.2 GB / 256.0 GB

Offload

10%

Memory breakdown

Weights247.1 GB

KV Cache7.7 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsLlama 3.1 405B on AMD Instinct MI325X 256GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 11.9 tok/s decode · 16.2s TTFT (warm) · 30 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 22.2 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	A	Very compromised (needs ~19.1 GB host RAM)	12.3 tok/s	8598 ms	4K
Coding	A	Very compromised (needs ~22.2 GB host RAM)	11.9 tok/s	16226 ms	4K
Agentic Coding	A	Very compromised (needs ~28.2 GB host RAM)	11.3 tok/s	24980 ms	4K
Reasoning	A	Very compromised (needs ~22.2 GB host RAM)	11.9 tok/s	19176 ms	4K
RAG	A	Very compromised (needs ~28.2 GB host RAM)	11.3 tok/s

Quantization options

How Llama 3.1 405B (405B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	158.0 GB	Low	A82
Q3_K_SBest for your GPU	3	198.5 GB	Low	A82

Get started

Copy-paste commands to run Llama 3.1 405B on your machine.

Run

ollama run llama3.1:405b

Frequently asked questions

Can AMD Instinct MI325X 256GB run Llama 3.1 405B?

Yes, AMD Instinct MI325X 256GB can run Llama 3.1 405B with a A grade (Very compromised (needs ~22.2 GB host RAM)). Expected decode speed: 11.9 tok/s.

How much VRAM does Llama 3.1 405B need?

Llama 3.1 405B (405B parameters) requires approximately 281.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 405B?

The recommended quantization for Llama 3.1 405B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 405B run at on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Llama 3.1 405B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16226ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run Llama 3.1 405B for coding?

For coding workloads, Llama 3.1 405B on AMD Instinct MI325X 256GB receives a A grade with 11.9 tok/s and 4K context.

What context window can Llama 3.1 405B use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Llama 3.1 405B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 405B feels slow on AMD Instinct MI325X 256GB?

See all results for AMD Instinct MI325X 256GB See all hardware for Llama 3.1 405B

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