Can Granite Code 20B run on RTX 4000 Ada 20GB?

YES — Tight Fit

A79Great

Estimated from fit model

Granite Code 20B needs ~18.6 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 18.6 GB, 24.9 tok/s, Tight fit

18.6 GB required20.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

24.9 tok/s

TTFT

7788 ms

Safe context

Memory

18.6 GB / 20.0 GB

Memory breakdown

Weights12.2 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsGranite Code 20B 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: 24.9 tok/s decode · 7.8s TTFT (warm) · 62 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	A	Tight fit	24.9 tok/s	4248 ms	8K
Coding	A	Tight fit	24.9 tok/s	7788 ms	8K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	15.6 tok/s	18018 ms	8K
Reasoning	A	Tight fit	24.9 tok/s	9204 ms	8K
RAG	B	Very compromised (needs ~1 GB host RAM)	15.6 tok/s	22522 ms	8K

Quantization options

How Granite Code 20B (20B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	A79
Q3_K_S	3	9.8 GB	Low	A81
NVFP4	4	11.2 GB	Medium	A80
Q4_K_M	4	12.2 GB	Medium	A80
Q5_K_MBest for your GPU	5	14.4 GB	High	A80
Q6_K	6	16.4 GB	High	F0
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 20B on your machine.

Run

ollama run granite-code:20b

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.2 tok/s
Qwen 3.5 27B	27B	A	10.4 tok/s
Qwen 3.6 27B	27B	S	13 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	24.6 tok/s
Magistral Small 2507	24B	S	15 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Granite Code 20B?

Yes, RTX 4000 Ada 20GB can run Granite Code 20B with a A grade (Tight fit). Expected decode speed: 24.9 tok/s.

How much VRAM does Granite Code 20B need?

Granite Code 20B (20B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 20B?

The recommended quantization for Granite Code 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 20B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Granite Code 20B achieves approximately 24.9 tokens per second decode speed with a time-to-first-token of 7788ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Granite Code 20B for coding?

For coding workloads, Granite Code 20B on RTX 4000 Ada 20GB receives a A grade with 24.9 tok/s and 8K context.

What context window can Granite Code 20B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Granite Code 20B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 20B feels slow on RTX 4000 Ada 20GB?

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

See all results for RTX 4000 Ada 20GB See all hardware for Granite Code 20B

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