Can granite 8b code instruct 4k run on RTX 2000 Ada Laptop 8GB?

Q: What should I upgrade first if granite 8b code instruct 4k feels slow on RTX 2000 Ada Laptop 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C51Usable

Estimated from fit model

granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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) — 7.8 GB, 38.3 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

38.3 tok/s

TTFT

5055 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on RTX 2000 Ada Laptop 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: 38.3 tok/s decode · 5.1s TTFT (warm) · 96 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement 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	38.3 tok/s	2758 ms	19K
Coding	C	Runs with offload	38.3 tok/s	5055 ms	19K
Agentic Coding	D	Very compromised (needs ~0.4 GB host RAM)	23.8 tok/s	11854 ms	19K
Reasoning	C	Runs with offload	38.3 tok/s	5975 ms	19K
RAG	D	Very compromised (needs ~0.4 GB host RAM)	23.8 tok/s	14818 ms

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C53
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

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+104)

Raises estimated decode speed by about 27%.48.7 tok/s decode

Raises estimated decode speed by about 27%.

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+192)

Raises estimated decode speed by about 49%.56.9 tok/s decode

Raises estimated decode speed by about 49%.

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

~$449 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 127%.86.8 tok/s decode

Raises estimated decode speed by about 127%.

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

~$549 MSRP

Frequently asked questions

Can RTX 2000 Ada Laptop 8GB run granite 8b code instruct 4k?

Yes, RTX 2000 Ada Laptop 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 38.3 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 7.8 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 RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, granite 8b code instruct 4k achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5055ms using Q4_K_M quantization.

Can RTX 2000 Ada Laptop 8GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RTX 2000 Ada Laptop 8GB receives a C grade with 38.3 tok/s and 19K context.

What context window can granite 8b code instruct 4k use on RTX 2000 Ada Laptop 8GB?

On RTX 2000 Ada Laptop 8GB, granite 8b code instruct 4k can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2000 Ada Laptop 8GB See all hardware for granite 8b code instruct 4k

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