Would CUDA be a better path than Intel Arc A770 16GB 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.

Can granite 8b code instruct 4k run on Intel Arc A770 16GB?

Q: What should I upgrade first if granite 8b code instruct 4k 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.

YES — Runs Great

C51Usable

Estimated from fit model

granite 8b code instruct 4k needs ~8.3 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 8.3 GB, 51.6 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

51.6 tok/s

TTFT

3749 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k 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: 51.6 tok/s decode · 3.7s TTFT (warm) · 129 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	51.6 tok/s	2045 ms	147K
Coding	C	Runs well	51.6 tok/s	3749 ms	147K
Agentic Coding	C	Runs well	51.6 tok/s	5453 ms	147K
Reasoning	C	Runs well	51.6 tok/s	4431 ms	147K
RAG	C	Runs well	51.6 tok/s	6817 ms	147K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4

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

Upgrade options

Hardware that runs granite 8b code instruct 4k well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 91%.98.4 tok/s decode

Raises estimated decode speed by about 91%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+80)

Raises estimated decode speed by about 98%.102.3 tok/s decode

Raises estimated decode speed by about 98%.

Adds memory headroom for longer context windows and future model growth.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

~$2,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run granite 8b code instruct 4k?

Yes, Intel Arc A770 16GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 51.6 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 8.3 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 A770 16GB?

On Intel Arc A770 16GB, granite 8b code instruct 4k achieves approximately 51.6 tokens per second decode speed with a time-to-first-token of 3749ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Intel Arc A770 16GB receives a C grade with 51.6 tok/s and 147K context.

What context window can granite 8b code instruct 4k use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, granite 8b code instruct 4k can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for granite 8b code instruct 4k

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