Will It Run AI

Can granite 8b code instruct 4k run on Intel Arc B580 12GB?

YES — Runs Great

C54Usable
Estimated from fit model

granite 8b code instruct 4k needs ~7.9 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 7.9 GB, 44.9 tok/s, Runs well
7.9 GB required12.0 GB available
66% VRAM used

Fit status

Runs well

Decode

44.9 tok/s

TTFT

4316 ms

Safe context

86K

Memory

7.9 GB / 12.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Intel Arc B580 12GB
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: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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
ChatCRuns well44.9 tok/s2354 ms86K
CodingCRuns well44.9 tok/s4316 ms86K
Agentic CodingCRuns well44.9 tok/s6278 ms86K
ReasoningCRuns well44.9 tok/s5101 ms86K
RAGCRuns well44.9 tok/s7848 ms86K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC49
Q3_K_S
3
3.9 GB
LowC50
NVFP4
4
4.5 GB
MediumC51
Q4_K_M
4
4.9 GB
MediumC52
Q5_K_M
5
5.8 GB
HighC52
Q6_K
6
6.6 GB
HighC52
Q8_0Best for your GPU
8
8.6 GB
Very HighC51
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Frequently asked questions

Can Intel Arc B580 12GB run granite 8b code instruct 4k?

Yes, Intel Arc B580 12GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 44.9 tok/s.

How much VRAM does granite 8b code instruct 4k need?

granite 8b code instruct 4k (8B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for granite 8b code instruct 4k?

The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.

What speed will granite 8b code instruct 4k run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, granite 8b code instruct 4k achieves approximately 44.9 tokens per second decode speed with a time-to-first-token of 4316ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Intel Arc B580 12GB receives a C grade with 44.9 tok/s and 86K context.

What context window can granite 8b code instruct 4k use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, granite 8b code instruct 4k can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if granite 8b code instruct 4k feels slow on Intel Arc B580 12GB?

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.

Would CUDA be a better path than Intel Arc B580 12GB for granite 8b code instruct 4k?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

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