Can Granite Code 34B run on RTX 5090 Laptop 24GB?

Q: What should I upgrade first if Granite Code 34B feels slow on RTX 5090 Laptop 24GB?

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.

BARELY — Tight on Memory

B66Good

Estimated from fit model

Granite Code 34B needs ~27.7 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.7 GB, 21.8 tok/s, Very compromised (needs ~2.8 GB host RAM)

27.7 GB required24.0 GB available

115% VRAM needed

3.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.8 GB host RAM)

Decode

21.8 tok/s

TTFT

8881 ms

Safe context

Memory

27.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite Code 34B on RTX 5090 Laptop 24GB
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: 21.8 tok/s decode · 8.9s TTFT (warm) · 55 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 2.8 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	B	Runs with offload (needs ~1.5 GB host RAM)	25.2 tok/s	4195 ms	4K
Coding	B	Very compromised (needs ~2.8 GB host RAM)	21.8 tok/s	8881 ms	4K
Agentic Coding	F	Too heavy	16.8 tok/s	16776 ms	4K
Reasoning	B	Very compromised (needs ~2.8 GB host RAM)	21.8 tok/s	10495 ms	4K
RAG	F	Too heavy	16.8 tok/s	20970 ms

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	A77
Q3_K_SBest for your GPU	3	16.7 GB	Low	A76

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 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+896)

Removes host-memory offload, which is usually the single biggest latency and throughput win.40.6 tok/s decode

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

Raises estimated decode speed by about 86%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.3 tok/s decode

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

Raises estimated decode speed by about 80%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.24.1 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can RTX 5090 Laptop 24GB run Granite Code 34B?

Yes, RTX 5090 Laptop 24GB can run Granite Code 34B with a B grade (Very compromised (needs ~2.8 GB host RAM)). Expected decode speed: 21.8 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Granite Code 34B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8881ms using Q4_K_M quantization.

Can RTX 5090 Laptop 24GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on RTX 5090 Laptop 24GB receives a B grade with 21.8 tok/s and 4K context.

What context window can Granite Code 34B use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, Granite Code 34B can safely use up to 4K 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 34B feels slow on RTX 5090 Laptop 24GB?

See all results for RTX 5090 Laptop 24GB See all hardware for Granite Code 34B

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