Can CodeLlama 7B Instruct run on RX 6600 XT 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeLlama 7B Instruct needs ~13.8 GB but RX 6600 XT 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: 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) — 13.8 GB, exceeds 8.0 GB available

13.8 GB required8.0 GB available

173% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.2 tok/s

TTFT

27061 ms

Safe context

Memory

13.8 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 7B Instruct on RX 6600 XT 8GB
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: 7.2 tok/s decode · 27.1s TTFT (warm) · 18 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.8 GB, but this setup only exposes 8.0 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	F	Too heavy	14.4 tok/s	7319 ms	4K
Coding	F	Too heavy	7.2 tok/s	27061 ms	4K
Agentic Coding	F	Too heavy	4.5 tok/s	62627 ms	4K
Reasoning	F	Too heavy	7.2 tok/s	31981 ms	4K
RAG	F	Too heavy	4.5 tok/s	78283 ms	4K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RX 6600 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A77
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	A77
Q4_K_M	4	4.3 GB	Medium	A76
Q5_K_MBest for your GPU	5	5.0 GB	High	A76
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Upgrade-Optionen

Hardware, die CodeLlama 7B Instruct gut ausführt

RX 7600 XT 16GBGünstige Wahl

16 GB VRAM (+8)288 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.39.1 tok/s Dekodierung

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.

ca. $329 MSRP

RX 9060 XT 16GBBestes Preis-Leistungs-Verhältnis

16 GB VRAM (+8)320 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s Dekodierung

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.

ca. $349 MSRP

RX 7700 XT 12GBAMD-Upgrade

12 GB VRAM (+4)432 GB/s (+176)

Makes the model fit on the accelerator instead of staying completely out of reach.32 tok/s Dekodierung

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

Raises estimated decode speed by about 344%.

ca. $449 MSRP

RX 7900 XT 20GBGrößter Sprung

20 GB VRAM (+12)800 GB/s (+544)

Makes the model fit on the accelerator instead of staying completely out of reach.98 tok/s Dekodierung

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.

ca. $899 MSRP

Frequently asked questions

Can RX 6600 XT 8GB run CodeLlama 7B Instruct?

No, CodeLlama 7B Instruct requires more memory than RX 6600 XT 8GB provides.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 13.8 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 RX 6600 XT 8GB?

On RX 6600 XT 8GB, CodeLlama 7B Instruct achieves approximately 7.2 tokens per second decode speed with a time-to-first-token of 27061ms using Q4_K_M quantization.

Can RX 6600 XT 8GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RX 6600 XT 8GB receives a F grade with 7.2 tok/s and 4K context.

What context window can CodeLlama 7B Instruct use on RX 6600 XT 8GB?

On RX 6600 XT 8GB, CodeLlama 7B Instruct can safely use up to 4K 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 7B Instruct feels slow on RX 6600 XT 8GB?

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 6600 XT 8GB See all hardware for CodeLlama 7B Instruct

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