Can Granite Code 34B run on RTX 4000 Ada 20GB?

YES — With Q3_K_S

B63Good

○Estimated from fit model

Granite Code 34B needs ~23.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q3_K_S quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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 4000 Ada 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

5.6 tok/s

TTFT

34674 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 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: 5.6 tok/s decode · 34.7s TTFT (warm) · 14 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	6.0 tok/s	17733 ms	4K
Coding	F	Too heavy	5.2 tok/s	37564 ms	4K
Agentic Coding	F	Too heavy	4.0 tok/s	71022 ms	4K
Reasoning	F	Too heavy	5.2 tok/s	44394 ms	4K
RAG	F	Too heavy	4.0 tok/s	88777 ms	4K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RTX 4000 Ada 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

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

👁 NVIDIA
RTX 3090 24GBBest value

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

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 232%.

~$1,499 MSRP

👁 NVIDIA
RTX 4090 24GBNVIDIA upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.21.7 tok/s decode

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

Raises estimated decode speed by about 288%.

~$1,599 MSRP

👁 NVIDIA
RTX 5090 32GBBudget pick

32 GB VRAM (+12)1792 GB/s (+1432)

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

Frequently asked questions

See all results for RTX 4000 Ada 20GB See all hardware for Granite Code 34B

URL: https://willitrunai.com/can-run/granite-code-34b-on-rtx-4000-ada-20gb

⇱

Can Granite Code 34B run on RTX 4000 Ada 20GB?

YES — With Q3_K_S

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs Granite Code 34B well

Frequently asked questions