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⇱ Granite Code 20B on Intel Arc Pro B50 16GB? YES

Can Granite Code 20B run on Intel Arc Pro B50 16GB?

BARELY — Tight on Memory

B65Good
○Estimated from fit model

Granite Code 20B needs ~17.9 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 17.9 GB, 6.5 tok/s, Very compromised (needs ~1.3 GB host RAM)
17.9 GB required16.0 GB available
112% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.3 GB host RAM)

Decode

6.5 tok/s

TTFT

29636 ms

Safe context

7K

Memory

17.9 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights12.2 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 6.5 tok/s decode · 29.6s TTFT (warm) · 16 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns with offload (needs ~0.2 GB host RAM)7.9 tok/s13372 ms7K
CodingBVery compromised (needs ~1.3 GB host RAM)6.5 tok/s29636 ms7K
Agentic CodingFToo heavy4.7 tok/s60168 ms7K
ReasoningBVery compromised (needs ~1.3 GB host RAM)6.5 tok/s35025 ms7K
RAGFToo heavy4.7 tok/s75210 ms7K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowA81
Q3_K_S
3
9.8 GB
LowA81
NVFP4
4
11.2 GB
MediumA80
Q4_K_MBest for your GPU
4
12.2 GB
MediumA80
Q5_K_M
5
14.4 GB
HighF0
Q6_K
6
16.4 GB
HighF0
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Upgrade options

Hardware that runs Granite Code 20B well

👁 Intel
Intel Arc Pro B60 24GBBudget pick
24 GB VRAM (+8)456 GB/s (+232)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.21.8 tok/s decode

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

Raises estimated decode speed by about 235%.

~$599 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBest value
128 GB VRAM (+112)3200 GB/s (+2976)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.178.5 tok/s decode

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

Raises estimated decode speed by about 2646%.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBIntel upgrade
128 GB VRAM (+112)3700 GB/s (+3476)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.229.3 tok/s decode

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

Raises estimated decode speed by about 3428%.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc Pro B50 16GBSee all hardware for Granite Code 20B