Can Codestral 22B v0.1 IMat run on RTX 3500 Ada Laptop 12GB?

Q: What should I upgrade first if Codestral 22B v0.1 IMat feels slow on RTX 3500 Ada Laptop 12GB?

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.

YES — With Q2_K

D37Poor

Estimated from fit model

Codestral 22B v0.1 IMat needs ~13.6 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~14 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.

Codestral 22B v0.1 IMat at Q4_K_M needs 18.4 GB — too much for RTX 3500 Ada Laptop 12GB (12.0 GB). Runs at Q2_K (13.6 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 18.4 GB, exceeds 12.0 GB available

18.4 GB required12.0 GB available

153% VRAM needed

6.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.6 tok/s

TTFT

34722 ms

Safe context

Memory

18.4 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral 22B v0.1 IMat on RTX 3500 Ada Laptop 12GB
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 1.0 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	6.5 tok/s	16254 ms	4K
Coding	F	Too heavy	5.6 tok/s	34722 ms	4K
Agentic Coding	F	Too heavy	4.2 tok/s	66561 ms	4K
Reasoning	F	Too heavy	5.6 tok/s	41035 ms	4K
RAG	F	Too heavy	4.2 tok/s	83202 ms	4K

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	F0
Q3_K_S	3	10.8 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 IMat on your machine.

Run

lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

RTX 5060 Ti 16GBBest value

16 GB VRAM (+4)448 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.11.4 tok/s decode

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

Raises estimated decode speed by about 104%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.8.4 tok/s decode

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

Raises estimated decode speed by about 50%.

~$499 MSRP

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+8)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.20.9 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.

~$1,250 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+20)1792 GB/s (+1456)

Makes the model fit on the accelerator instead of staying completely out of reach.89.5 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

Can RTX 3500 Ada Laptop 12GB run Codestral 22B v0.1 IMat?

Yes, RTX 3500 Ada Laptop 12GB can run Codestral 22B v0.1 IMat at Q2_K quantization (Very compromised (needs ~1 GB host RAM)). The recommended Q4_K_M requires 18.4 GB which exceeds available memory, but at Q2_K it needs only 13.6 GB. Expected decode speed: 14.1 tok/s.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 18.4 GB at Q4_K_M quantization. On RTX 3500 Ada Laptop 12GB, it fits at Q2_K using 13.6 GB.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization is Q4_K_M, but on RTX 3500 Ada Laptop 12GB the best fitting quantization is Q2_K, which uses 13.6 GB.

What speed will Codestral 22B v0.1 IMat run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Codestral 22B v0.1 IMat achieves approximately 14.1 tokens per second decode speed with a time-to-first-token of 13736ms using Q2_K quantization.

Can RTX 3500 Ada Laptop 12GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on RTX 3500 Ada Laptop 12GB receives a F grade with 5.6 tok/s and 4K context.

What context window can Codestral 22B v0.1 IMat use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Codestral 22B v0.1 IMat can safely use up to 6K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Codestral 22B v0.1 IMat

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