Can granite 8b code instruct 4k run on RTX 4000 Ada 20GB?

YES — Runs Great

C50Usable

Estimated from fit model

granite 8b code instruct 4k needs ~9.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.0 GB, 57.5 tok/s, Runs well

9.0 GB required20.0 GB available

45% VRAM used

Fit status

Runs well

Decode

57.5 tok/s

TTFT

3365 ms

Safe context

203K

Memory

9.0 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on RTX 4000 Ada 20GB
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: 57.5 tok/s decode · 3.4s TTFT (warm) · 144 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	C	Runs well	57.5 tok/s	1835 ms	203K
Coding	C	Runs well	57.5 tok/s	3365 ms	203K
Agentic Coding	C	Runs well	57.5 tok/s	4894 ms	203K
Reasoning	C	Runs well	57.5 tok/s	3976 ms	203K
RAG	C	Runs well	57.5 tok/s	6117 ms	203K

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 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C45
Q3_K_S	3	3.9 GB	Low	C46
NVFP4	4	4.5 GB	Medium	C46
Q4_K_M	4	4.9 GB	Medium	C47
Q5_K_M	5	5.8 GB	High	C47
Q6_K	6	6.6 GB	High	C48
Q8_0Best for your GPU	8	8.6 GB	Very High	C49
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

Opções de upgrade

Hardware que roda bem granite 8b code instruct 4k

RTX 3090 24GBOpção econômica

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 95%.112 tok/s decodificação

Raises estimated decode speed by about 95%.

~$1,499 MSRP

RTX 4090 24GBMelhor custo-benefício

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 95%.112 tok/s decodificação

Raises estimated decode speed by about 95%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 95%.112 tok/s decodificação

Raises estimated decode speed by about 95%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run granite 8b code instruct 4k?

Yes, RTX 4000 Ada 20GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 57.5 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 9.0 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 4000 Ada 20GB?

On RTX 4000 Ada 20GB, granite 8b code instruct 4k achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RTX 4000 Ada 20GB receives a C grade with 57.5 tok/s and 203K context.

What context window can granite 8b code instruct 4k use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, granite 8b code instruct 4k can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for granite 8b code instruct 4k

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