Can CodeLlama 7B Instruct run on Radeon Pro W7800 32GB?

YES — Runs Great

A76Great

Estimated from fit model

CodeLlama 7B Instruct needs ~16.2 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 16.2 GB, 79.6 tok/s, Runs well

16.2 GB required32.0 GB available

51% VRAM used

Fit status

Runs well

Decode

79.6 tok/s

TTFT

2433 ms

Safe context

16K

Memory

16.2 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on Radeon Pro W7800 32GB
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: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	79.6 tok/s	1327 ms	16K
Coding	A	Runs well	79.6 tok/s	2433 ms	16K
Agentic Coding	A	Runs well	79.6 tok/s	3538 ms	16K
Reasoning	A	Runs well	79.6 tok/s	2875 ms	16K
RAG	A	Runs well	79.6 tok/s	4423 ms	16K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B66
NVFP4	4	3.9 GB	Medium	B67
Q4_K_M	4	4.3 GB	Medium	B67
Q5_K_M	5	5.0 GB	High	B67
Q6_K	6	5.7 GB	High	B67
Q8_0	8	7.5 GB	Very High	B68
F16Best for your GPU	16	14.3 GB	Maximum	A71

Get started

Copy-paste commands to run CodeLlama 7B Instruct on your machine.

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Your hardware

More models your Radeon Pro W7800 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
Qwen 3.5 27B	27B	S	22.3 tok/s
Qwen 3.6 27B	27B	S	16.9 tok/s
Qwen 3.6 35B A3B	35B	S	43.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.1 tok/s

Frequently asked questions

Can Radeon Pro W7800 32GB run CodeLlama 7B Instruct?

Yes, Radeon Pro W7800 32GB can run CodeLlama 7B Instruct with a A grade (Runs well). Expected decode speed: 79.6 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 16.2 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 7B Instruct run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, CodeLlama 7B Instruct achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on Radeon Pro W7800 32GB receives a A grade with 79.6 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, CodeLlama 7B 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 W7800 32GB See all hardware for CodeLlama 7B Instruct

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