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URL: https://willitrunai.com/can-run/codellama-13b-instruct-on-arc-pro-b50-16gb

⇱ CodeLlama 13B Instruct on Intel Arc Pro B50 16GB? No — Alte…

Can CodeLlama 13B Instruct run on Intel Arc Pro B50 16GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

CodeLlama 13B Instruct needs ~22.6 GB but Intel Arc Pro B50 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: 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) — 22.6 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

5.7 tok/s

TTFT

33688 ms

Safe context

7K

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 Intel Arc Pro B50 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: 5.7 tok/s decode · 33.7s TTFT (warm) · 14 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~0.3 GB host RAM)10.9 tok/s9680 ms7K
CodingFToo heavy5.7 tok/s33688 ms7K
Agentic CodingFToo heavy2.4 tok/s118116 ms7K
ReasoningFToo heavy5.7 tok/s39813 ms7K
RAGFToo heavy2.4 tok/s147645 ms7K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on Intel Arc Pro B50 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

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

👁 Intel
Intel Arc Pro B60 24GBBudget pick
24 GB VRAM (+8)456 GB/s (+232)
A
Makes the model fit on the accelerator instead of staying completely out of reach.31.1 tok/s decode

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.

~$599 MSRP

👁 NVIDIA
RTX 5090 32GBBiggest leap
32 GB VRAM (+16)1792 GB/s (+1568)
A
Makes the model fit on the accelerator instead of staying completely out of reach.146.9 tok/s decode

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.

~$1,999 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBest value
128 GB VRAM (+112)3200 GB/s (+2976)
A
Makes the model fit on the accelerator instead of staying completely out of reach.182 tok/s decode

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.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBIntel upgrade
128 GB VRAM (+112)3700 GB/s (+3476)
A
Makes the model fit on the accelerator instead of staying completely out of reach.182 tok/s decode

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.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc Pro B50 16GBSee all hardware for CodeLlama 13B Instruct