Will It Run AI

Can CodeLlama 13B Instruct run on RTX 5060 Ti 16GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

CodeLlama 13B Instruct needs ~22.9 GB but RTX 5060 Ti 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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) — 22.9 GB, exceeds 16.0 GB available
22.9 GB required16.0 GB available
143% VRAM needed

6.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.8 tok/s

TTFT

15070 ms

Safe context

7K

Memory

22.9 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights7.9 GB
KV Cache12.2 GB
Runtime1.2 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 RTX 5060 Ti 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: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.9 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload24.1 tok/s4373 ms7K
CodingFToo heavy12.8 tok/s15070 ms7K
Agentic CodingFToo heavy5.4 tok/s52345 ms7K
ReasoningFToo heavy12.8 tok/s17810 ms7K
RAGFToo heavy5.4 tok/s65431 ms7K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on RTX 5060 Ti 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.1 GB
LowA74
Q3_K_S
3
6.4 GB
LowA75
NVFP4
4
7.3 GB
MediumA76
Q4_K_M
4
7.9 GB
MediumA77
Q5_K_M
5
9.4 GB
HighA76
Q6_KBest for your GPU
6
10.7 GB
HighA76
Q8_0
8
13.9 GB
Very HighF0
F16
16
26.7 GB
MaximumF0

Opciones de mejora

Hardware que ejecuta bien CodeLlama 13B Instruct

NVIDIARTX 4000 Ada 20GBMejor relación calidad-precio
20 GB VRAM (+4)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.19.2 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Sube la velocidad estimada de decodificación alrededor de un 50%.

~$1,250 MSRP

NVIDIARTX 3090 24GBOpción económica
24 GB VRAM (+8)936 GB/s (+488)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.82.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,499 MSRP

NVIDIARTX 4090 24GBMejora NVIDIA
24 GB VRAM (+8)1008 GB/s (+560)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.96.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX 5060 Ti 16GB run CodeLlama 13B Instruct?

No, CodeLlama 13B Instruct requires more memory than RTX 5060 Ti 16GB provides.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 22.9 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 Ti 16GB?

On RTX 5060 Ti 16GB, CodeLlama 13B Instruct achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15070ms using Q4_K_M quantization.

Can RTX 5060 Ti 16GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on RTX 5060 Ti 16GB receives a F grade with 12.8 tok/s and 7K context.

What context window can CodeLlama 13B Instruct use on RTX 5060 Ti 16GB?

On RTX 5060 Ti 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 RTX 5060 Ti 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 RTX 5060 Ti 16GBSee all hardware for CodeLlama 13B Instruct
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