Can CodeLlama 13B Instruct run on AMD Instinct MI350X 288GB?

YES — Runs Great

A71Great

Estimated from fit model

CodeLlama 13B Instruct needs ~49.8 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~182 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 49.8 GB, 182.0 tok/s, Runs well

49.8 GB required288.0 GB available

17% VRAM used

Fit status

Runs well

Decode

182.0 tok/s

TTFT

1064 ms

Safe context

16K

Memory

49.8 GB / 288.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on AMD Instinct MI350X 288GB
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: 182.0 tok/s decode · 1.1s TTFT (warm) · 455 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	182.0 tok/s	580 ms	16K
Coding	A	Runs well	182.0 tok/s	1064 ms	16K
Agentic Coding	A	Runs well	182.0 tok/s	1547 ms	16K
Reasoning	A	Runs well	182.0 tok/s	1257 ms	16K
RAG	A	Runs well	182.0 tok/s	1934 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B61
Q3_K_S	3	6.4 GB	Low	B61
NVFP4	4

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Your hardware

More models your AMD Instinct MI350X 288GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
Devstral 2 123B Instruct	123B	S

Frequently asked questions

Can AMD Instinct MI350X 288GB run CodeLlama 13B Instruct?

Yes, AMD Instinct MI350X 288GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 182.0 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 49.8 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, CodeLlama 13B Instruct achieves approximately 182.0 tokens per second decode speed with a time-to-first-token of 1064ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on AMD Instinct MI350X 288GB receives a A grade with 182.0 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, CodeLlama 13B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for CodeLlama 13B Instruct

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