Can CodeLlama 13B Instruct run on RX 6900 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeLlama 13B Instruct needs ~22.6 GB but RX 6900 XT 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: StandardBottleneck: Memory capacity

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) — 22.6 GB, exceeds 16.0 GB available

22.6 GB required16.0 GB available

141% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.3 tok/s

TTFT

14561 ms

Safe context

Memory

22.6 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct on RX 6900 XT 16GB
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: 13.3 tok/s decode · 14.6s TTFT (warm) · 33 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.6 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.3 GB host RAM)	25.8 tok/s	4099 ms	7K
Coding	F	Too heavy	13.3 tok/s	14561 ms	7K
Agentic Coding	F	Too heavy	5.5 tok/s	51012 ms	7K
Reasoning	F	Too heavy	13.3 tok/s	17209 ms	7K
RAG	F	Too heavy	5.5 tok/s	63765 ms	7K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RX 6900 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	A74
Q3_K_S	3	6.4 GB	Low	A75
NVFP4	4	7.3 GB	Medium	A76
Q4_K_M	4	7.9 GB	Medium	A77
Q5_K_M	5	9.4 GB	High	A76
Q6_KBest for your GPU	6	10.7 GB	High	A76
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

アップグレードオプション

CodeLlama 13B Instructを快適に動かすハードウェア

RX 7900 XT 20GBコスパ最良

20 GB VRAM (+4)800 GB/s (+288)

Makes the model fit on the accelerator instead of staying completely out of reach.33.7 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 153%.

〜$899 MSRP

RX 7900 XTX 24GBコスパ重視

24 GB VRAM (+8)960 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.87.2 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$999 MSRP

Radeon AI PRO R9700 32GBAMDアップグレード

32 GB VRAM (+16)640 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.47.6 tok/sデコード

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

〜$1,899 MSRP

Frequently asked questions

Can RX 6900 XT 16GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than RX 6900 XT 16GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 22.6 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 RX 6900 XT 16GB?

On RX 6900 XT 16GB, CodeLlama 13B Instruct achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14561ms using Q4_K_M quantization.

Can RX 6900 XT 16GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RX 6900 XT 16GB receives a F grade with 13.3 tok/s and 7K context.

What context window can CodeLlama 13B Instruct use on RX 6900 XT 16GB?

On RX 6900 XT 16GB, CodeLlama 13B Instruct can safely use up to 7K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if CodeLlama 13B Instruct feels slow on RX 6900 XT 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 6900 XT 16GB See all hardware for CodeLlama 13B Instruct

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