Can Codestral 2 25.08 run on RTX 5000 Ada Laptop 16GB?

Q: What should I upgrade first if Codestral 2 25.08 feels slow on RTX 5000 Ada Laptop 16GB?

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

A72Great

Estimated from fit model

Codestral 2 25.08 needs ~18.4 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 18.4 GB, 12.8 tok/s, Very compromised (needs ~1.7 GB host RAM)

18.4 GB required16.0 GB available

115% VRAM needed

2.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.7 GB host RAM)

Decode

12.8 tok/s

TTFT

15081 ms

Safe context

Memory

18.4 GB / 16.0 GB

Offload

10%

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 on RTX 5000 Ada Laptop 16GB
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: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 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 1.7 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	A	Runs with offload (needs ~0.9 GB host RAM)	14.8 tok/s	7117 ms	4K
Coding	A	Very compromised (needs ~1.7 GB host RAM)	12.8 tok/s	15081 ms	4K
Agentic Coding	F	Too heavy	9.9 tok/s	28530 ms	4K
Reasoning	A	Very compromised (needs ~1.7 GB host RAM)	12.8 tok/s	17824 ms	4K
RAG	F	Too heavy	9.9 tok/s	35662 ms

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	S86
Q3_K_SBest for your GPU	3	10.8 GB	Low	S85

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Codestral 2 25.08?

Yes, RTX 5000 Ada Laptop 16GB can run Codestral 2 25.08 with a A grade (Very compromised (needs ~1.7 GB host RAM)). Expected decode speed: 12.8 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 18.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Codestral 2 25.08 achieves approximately 12.8 tokens per second decode speed with a time-to-first-token of 15081ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on RTX 5000 Ada Laptop 16GB receives a A grade with 12.8 tok/s and 4K context.

What context window can Codestral 2 25.08 use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Codestral 2 25.08 can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Codestral 2 25.08

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