Can granite 8b code instruct 4k run on RTX 3080 12GB?

YES — Runs Great

B56Good

Estimated from fit model

granite 8b code instruct 4k needs ~8.2 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 8.2 GB, 112.0 tok/s, Runs well

8.2 GB required12.0 GB available

68% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

81K

Memory

8.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on RTX 3080 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	112.0 tok/s	943 ms	81K
Coding	B	Runs well	112.0 tok/s	1729 ms	81K
Agentic Coding	B	Runs well	112.0 tok/s	2514 ms	81K
Reasoning	B	Runs well	112.0 tok/s	2043 ms	81K
RAG	B	Runs well	112.0 tok/s	3143 ms	81K

Inference speed

granite 8b code instruct 4k inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for granite 8b code instruct 4k at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	95.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	90.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	77.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	76.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	76.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	49.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	48.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	45.1	Fits
RTX 4060 8GB	8 GB	Q4_K_M	40.7	Offloads
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	39.6	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 3080 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C49
Q3_K_S	3	3.9 GB	Low	C50
NVFP4	4	4.5 GB	Medium	C51
Q4_K_M	4	4.9 GB	Medium	C52
Q5_K_M	5	5.8 GB	High	C52
Q6_K	6	6.6 GB	High	C52
Q8_0Best for your GPU	8	8.6 GB	Very High	C51
F16	16	16.4 GB	Maximum	F0

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 RTX 3080 12GB run granite 8b code instruct 4k?

Yes, RTX 3080 12GB can run granite 8b code instruct 4k with a B grade (Runs well). Expected decode speed: 112.0 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 8.2 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 3080 12GB?

On RTX 3080 12GB, granite 8b code instruct 4k achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 12GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RTX 3080 12GB receives a B grade with 112.0 tok/s and 81K context.

What context window can granite 8b code instruct 4k use on RTX 3080 12GB?

On RTX 3080 12GB, granite 8b code instruct 4k can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3080 12GB See all hardware for granite 8b code instruct 4k

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-rtx-3080-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: