Can CodeLlama 13B Instruct run on Radeon Pro W7900 48GB?

YES — Runs Great

A77Great

Estimated from fit model

CodeLlama 13B Instruct needs ~25.8 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~64 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) — 25.8 GB, 64.3 tok/s, Runs well

25.8 GB required48.0 GB available

54% VRAM used

Fit status

Runs well

Decode

64.3 tok/s

TTFT

3012 ms

Safe context

16K

Memory

25.8 GB / 48.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on Radeon Pro W7900 48GB
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: 64.3 tok/s decode · 3.0s TTFT (warm) · 161 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	64.3 tok/s	1643 ms	16K
Coding	A	Runs well	64.3 tok/s	3012 ms	16K
Agentic Coding	A	Runs well	64.3 tok/s	4381 ms	16K
Reasoning	A	Runs well	64.3 tok/s	3559 ms	16K
RAG	A	Runs well	64.3 tok/s	5476 ms	16K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B67
Q3_K_S	3	6.4 GB	Low	B67
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 Radeon Pro W7900 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	77.1 tok/s
Qwen 3.5 27B	27B	S	33.4 tok/s

Frequently asked questions

Can Radeon Pro W7900 48GB run CodeLlama 13B Instruct?

Yes, Radeon Pro W7900 48GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 64.3 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 25.8 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, CodeLlama 13B Instruct achieves approximately 64.3 tokens per second decode speed with a time-to-first-token of 3012ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Radeon Pro W7900 48GB receives a A grade with 64.3 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, 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 Radeon Pro W7900 48GB See all hardware for CodeLlama 13B Instruct

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