Can Codestral 21B Pruned i1 run on RTX 3500 Ada Laptop 12GB?

Q: What should I upgrade first if Codestral 21B Pruned i1 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

D38Poor

Estimated from fit model

Codestral 21B Pruned i1 needs ~13.1 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~16 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 21B Pruned i1 at Q4_K_M needs 17.7 GB — too much for RTX 3500 Ada Laptop 12GB (12.0 GB). Runs at Q2_K (13.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.7 GB, exceeds 12.0 GB available

17.7 GB required12.0 GB available

148% VRAM needed

5.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.4 tok/s

TTFT

30446 ms

Safe context

Memory

17.7 GB / 12.0 GB

Offload

30%

Memory breakdown

Weights12.8 GB

KV Cache2.5 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 21B Pruned i1 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: 6.4 tok/s decode · 30.4s TTFT (warm) · 16 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 0.7 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	7.4 tok/s	14266 ms	4K
Coding	F	Too heavy	6.4 tok/s	30446 ms	4K
Agentic Coding	F	Too heavy	4.8 tok/s	58272 ms	4K
Reasoning	F	Too heavy	6.4 tok/s	35982 ms	4K
RAG	F	Too heavy	4.8 tok/s	72840 ms	4K

Inference speed

Codestral 21B Pruned i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 21B Pruned i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~94 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	93.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	59.8	Fits

Quantization options

How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	8.2 GB	Low	C51
Q3_K_S	3	10.3 GB	Low	F0

Get started

Copy-paste commands to run Codestral 21B Pruned i1 on your machine.

Run

lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server start

Upgrade options

Hardware that runs Codestral 21B Pruned i1 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.12.9 tok/s decode

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

Raises estimated decode speed by about 102%.

~$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.9.5 tok/s decode

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

Raises estimated decode speed by about 48%.

~$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.21.9 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 4090 24GBBiggest leap

24 GB VRAM (+12)1008 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.59.8 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.

~$1,599 MSRP

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Codestral 21B Pruned i1?

Yes, RTX 3500 Ada Laptop 12GB can run Codestral 21B Pruned i1 at Q2_K quantization (Very compromised (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 17.7 GB which exceeds available memory, but at Q2_K it needs only 13.1 GB. Expected decode speed: 16.0 tok/s.

How much VRAM does Codestral 21B Pruned i1 need?

Codestral 21B Pruned i1 (21B parameters) requires approximately 17.7 GB at Q4_K_M quantization. On RTX 3500 Ada Laptop 12GB, it fits at Q2_K using 13.1 GB.

What is the best quantization for Codestral 21B Pruned i1?

The recommended quantization is Q4_K_M, but on RTX 3500 Ada Laptop 12GB the best fitting quantization is Q2_K, which uses 13.1 GB.

What speed will Codestral 21B Pruned i1 run at on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Codestral 21B Pruned i1 achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12100ms using Q2_K quantization.

Can RTX 3500 Ada Laptop 12GB run Codestral 21B Pruned i1 for coding?

For coding workloads, Codestral 21B Pruned i1 on RTX 3500 Ada Laptop 12GB receives a F grade with 6.4 tok/s and 4K context.

What context window can Codestral 21B Pruned i1 use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Codestral 21B Pruned i1 can safely use up to 9K 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 21B Pruned i1

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