Can CodeLlama 13B Instruct run on RTX 5060 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

CodeLlama 13B Instruct needs ~22.1 GB but RTX 5060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: 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) — 22.1 GB, exceeds 8.0 GB available

22.1 GB required8.0 GB available

276% VRAM needed

14.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.2 tok/s

TTFT

37452 ms

Safe context

Memory

22.1 GB / 8.0 GB

Offload

60%

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodeLlama 13B Instruct on RTX 5060 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: 5.2 tok/s decode · 37.5s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.1 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	6.4 tok/s	16550 ms	4K
Coding	F	Too heavy	5.2 tok/s	37452 ms	4K
Agentic Coding	F	Too heavy	5.2 tok/s	54476 ms	4K
Reasoning	F	Too heavy	5.2 tok/s	44262 ms	4K
RAG	F	Too heavy	5.2 tok/s	68095 ms	4K

Inference speed

CodeLlama 13B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeLlama 13B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.6	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.2	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	82.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.8	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 5060 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	5.1 GB	Low	A78
Q3_K_S	3	6.4 GB	Low	F0
NVFP4	4	7.3 GB	Medium	F0
Q4_K_M	4	7.9 GB	Medium	F0
Q5_K_M	5	9.4 GB	High	F0
Q6_K	6	10.7 GB	High	F0
Q8_0	8	13.9 GB	Very High	F0
F16	16	26.7 GB	Maximum	F0

升级选项

能流畅运行 CodeLlama 13B Instruct 的硬件

RTX 4000 Ada 20GB最佳性价比

20 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.19.2 tok/s 解码

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

Raises estimated decode speed by about 269%.

~$1,250 MSRP

RTX 3090 24GB经济之选

24 GB VRAM (+16)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.82.6 tok/s 解码

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.

~$1,499 MSRP

RTX 4090 24GBNVIDIA 升级

24 GB VRAM (+16)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.96.6 tok/s 解码

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.

~$1,599 MSRP

Frequently asked questions

Can RTX 5060 8GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than RTX 5060 8GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 22.1 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 RTX 5060 8GB?

On RTX 5060 8GB, CodeLlama 13B Instruct achieves approximately 5.2 tokens per second decode speed with a time-to-first-token of 37452ms using Q4_K_M quantization.

Can RTX 5060 8GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX 5060 8GB receives a F grade with 5.2 tok/s and 4K context.

What context window can CodeLlama 13B Instruct use on RTX 5060 8GB?

On RTX 5060 8GB, CodeLlama 13B 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 13B Instruct feels slow on RTX 5060 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 RTX 5060 8GB See all hardware for CodeLlama 13B Instruct

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