Can Granite 3.1 8B run on Intel Arc A550M 8GB?

YES — With Offload

C43Usable

Estimated from fit model

Granite 3.1 8B needs ~8.5 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

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

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) — 8.5 GB, 18.2 tok/s, Runs with offload (needs ~0.3 GB host RAM)

8.5 GB required8.0 GB available

106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

18.2 tok/s

TTFT

10634 ms

Safe context

12K

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on Intel Arc A550M 8GB
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: 18.2 tok/s decode · 10.6s TTFT (warm) · 46 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	27.8 tok/s	3798 ms	12K
Coding	C	Runs with offload (needs ~0.3 GB host RAM)	18.2 tok/s	10634 ms	12K
Agentic Coding	F	Too heavy	11.8 tok/s	23869 ms	12K
Reasoning	C	Runs with offload (needs ~0.3 GB host RAM)	18.2 tok/s	12567 ms	12K
RAG	F	Too heavy	11.8 tok/s	29837 ms	12K

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B58
Q3_K_S	3	3.9 GB	Low	B58
NVFP4	4	4.5 GB	Medium	B58
Q4_K_MBest for your GPU	4	4.9 GB	Medium	B57
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run granite3.1-dense

升级选项

能流畅运行 Granite 3.1 8B 的硬件

Intel Arc B570 10GB经济之选

10 GB VRAM (+2)380 GB/s (+156)

Removes host-memory offload, which is usually the single biggest latency and throughput win.52 tok/s 解码

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

Raises estimated decode speed by about 186%.

~$219 MSRP

Intel Arc B580 12GB最佳性价比

12 GB VRAM (+4)456 GB/s (+232)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55.4 tok/s 解码

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

Raises estimated decode speed by about 204%.

~$249 MSRP

Intel Arc A770 16GBIntel 升级

16 GB VRAM (+8)560 GB/s (+336)

Removes host-memory offload, which is usually the single biggest latency and throughput win.63.8 tok/s 解码

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

Raises estimated decode speed by about 251%.

~$349 MSRP

Frequently asked questions

Can Intel Arc A550M 8GB run Granite 3.1 8B?

Yes, Intel Arc A550M 8GB can run Granite 3.1 8B with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 18.2 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 8.5 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 A550M 8GB?

On Intel Arc A550M 8GB, Granite 3.1 8B achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10634ms using Q4_K_M quantization.

Can Intel Arc A550M 8GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Intel Arc A550M 8GB receives a C grade with 18.2 tok/s and 12K context.

What context window can Granite 3.1 8B use on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, Granite 3.1 8B can safely use up to 12K 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 A550M 8GB?

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.

Would CUDA be a better path than Intel Arc A550M 8GB 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 A550M 8GB See all hardware for Granite 3.1 8B

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