Can Granite Code 34B run on RTX A4500 20GB?

YES — With Q3_K_S

B65Good

Estimated from fit model

Granite Code 34B needs ~23.5 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q3_K_S quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Granite Code 34B at Q4_K_M needs 27.6 GB — too much for RTX A4500 20GB (20.0 GB). Runs at Q3_K_S (23.5 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.6 GB, exceeds 20.0 GB available

27.6 GB required20.0 GB available

138% VRAM needed

7.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.9 tok/s

TTFT

19504 ms

Safe context

Memory

27.6 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGranite Code 34B on RTX A4500 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: 9.9 tok/s decode · 19.5s TTFT (warm) · 25 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.5 tok/s	9207 ms	4K
Coding	F	Too heavy	9.9 tok/s	19504 ms	4K
Agentic Coding	F	Too heavy	7.6 tok/s	36877 ms	4K
Reasoning	F	Too heavy	9.9 tok/s	23051 ms	4K
RAG	F	Too heavy	7.6 tok/s	46096 ms	4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	13.3 GB	Low	A77
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

Upgrade options

Hardware that runs Granite Code 34B well

RTX 3090 24GBBest value

24 GB VRAM (+4)936 GB/s (+296)

Makes the model fit on the accelerator instead of staying completely out of reach.18.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 88%.

~$1,499 MSRP

RTX 5090 32GBBudget pick

32 GB VRAM (+12)1792 GB/s (+1152)

Makes the model fit on the accelerator instead of staying completely out of reach.62.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

RTX A5500 24GBNVIDIA upgrade

24 GB VRAM (+4)768 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.17 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 72%.

~$3,200 MSRP

Frequently asked questions

Can RTX A4500 20GB run Granite Code 34B?

Yes, RTX A4500 20GB can run Granite Code 34B at Q3_K_S quantization (Very compromised (needs ~2.5 GB host RAM)). The recommended Q4_K_M requires 27.6 GB which exceeds available memory, but at Q3_K_S it needs only 23.5 GB. Expected decode speed: 16.1 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 27.6 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at Q3_K_S using 23.5 GB.

What is the best quantization for Granite Code 34B?

The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is Q3_K_S, which uses 23.5 GB.

What speed will Granite Code 34B run at on RTX A4500 20GB?

On RTX A4500 20GB, Granite Code 34B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 12031ms using Q3_K_S quantization.

Can RTX A4500 20GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX A4500 20GB receives a F grade with 9.9 tok/s and 4K context.

What context window can Granite Code 34B use on RTX A4500 20GB?

On RTX A4500 20GB, Granite Code 34B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on RTX A4500 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX A4500 20GB See all hardware for Granite Code 34B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/granite-code-34b-on-rtx-a4500-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

11.5 tok/s

9207 ms

Coding

Too heavy

9.9 tok/s

19504 ms

Agentic Coding

Too heavy

7.6 tok/s

36877 ms

Reasoning

Too heavy

9.9 tok/s

23051 ms

RAG

Too heavy

7.6 tok/s

46096 ms

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	13.3 GB	Low	A77
Q3_K_S	3	16.7 GB	Low	F0
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Frequently asked questions

Can RTX A4500 20GB run Granite Code 34B?

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 27.6 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at Q3_K_S using 23.5 GB.

What is the best quantization for Granite Code 34B?

The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is Q3_K_S, which uses 23.5 GB.

What speed will Granite Code 34B run at on RTX A4500 20GB?

On RTX A4500 20GB, Granite Code 34B achieves approximately 16.1 tokens per second decode speed with a time-to-first-token of 12031ms using Q3_K_S quantization.

Can RTX A4500 20GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX A4500 20GB receives a F grade with 9.9 tok/s and 4K context.

What context window can Granite Code 34B use on RTX A4500 20GB?

On RTX A4500 20GB, Granite Code 34B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Granite Code 34B feels slow on RTX A4500 20GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.