Will It Run AI

Can Granite 3.1 8B run on Intel Arc A770 16GB?

YES — Runs Great

B58Good
Estimated from fit model

Granite 3.1 8B needs ~9.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~64 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) — 9.3 GB, 63.8 tok/s, Runs well
9.3 GB required16.0 GB available
58% VRAM used

Fit status

Runs well

Decode

63.8 tok/s

TTFT

3033 ms

Safe context

71K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on Intel Arc A770 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: 63.8 tok/s decode · 3.0s TTFT (warm) · 160 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
ChatBRuns well63.8 tok/s1654 ms71K
CodingBRuns well63.8 tok/s3033 ms71K
Agentic CodingBRuns well63.8 tok/s4411 ms71K
ReasoningBRuns well63.8 tok/s3584 ms71K
RAGBRuns well63.8 tok/s5514 ms71K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC52
Q3_K_S
3
3.9 GB
LowC52
NVFP4
4
4.5 GB
MediumC53
Q4_K_M
4
4.9 GB
MediumC53
Q5_K_M
5
5.8 GB
HighC54
Q6_K
6
6.6 GB
HighC55
Q8_0Best for your GPU
8
8.6 GB
Very HighB56
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Frequently asked questions

Can Intel Arc A770 16GB run Granite 3.1 8B?

Yes, Intel Arc A770 16GB can run Granite 3.1 8B with a B grade (Runs well). Expected decode speed: 63.8 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Granite 3.1 8B achieves approximately 63.8 tokens per second decode speed with a time-to-first-token of 3033ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Intel Arc A770 16GB receives a B grade with 63.8 tok/s and 71K context.

What context window can Granite 3.1 8B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, Granite 3.1 8B can safely use up to 71K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 3.1 8B feels slow on Intel Arc A770 16GB?

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 A770 16GB for Granite 3.1 8B?

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.

See all results for Intel Arc A770 16GBSee all hardware for Granite 3.1 8B
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