VOOZH about

URL: https://willitrunai.com/can-run/codestral-2-25.08-on-a2-16gb

⇱

Can Codestral 2 25.08 run on NVIDIA A2 16GB?

BARELY — Tight on Memory

B69Good
○Estimated from fit model

Codestral 2 25.08 needs ~18.4 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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, 6.3 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

6.3 tok/s

TTFT

30907 ms

Safe context

4K

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 NVIDIA A2 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: 6.3 tok/s decode · 30.9s 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload6.7 tok/s15680 ms4K
CodingBVery compromised5.8 tok/s33225 ms4K
Agentic CodingFToo heavy4.5 tok/s62852 ms4K
ReasoningBVery compromised5.8 tok/s39266 ms4K
RAGFToo heavy4.5 tok/s78566 ms4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
8.6 GB
LowS86
Q3_K_SBest for your GPU
3
10.8 GB
LowS85

Get started

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

Run

lms load codestral-2508 && lms server start

Upgrade options

Hardware that runs Codestral 2 25.08 well

👁 NVIDIA
RTX 4000 Ada 20GBBudget pick
20 GB VRAM (+4)360 GB/s (+160)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.20.1 tok/s decode

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

Raises estimated decode speed by about 219%.

~$1,250 MSRP

👁 NVIDIA
RTX 3090 24GBBest value
24 GB VRAM (+8)936 GB/s (+736)
S
Removes host-memory offload, which is usually the single biggest latency and throughput win.38.2 tok/s decode

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

Raises estimated decode speed by about 506%.

~$1,499 MSRP

👁 NVIDIA
RTX PRO 4000 Blackwell 24GBNVIDIA upgrade
24 GB VRAM (+8)672 GB/s (+472)
S
Removes host-memory offload, which is usually the single biggest latency and throughput win.40.4 tok/s decode

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

Raises estimated decode speed by about 541%.

~$1,599 MSRP

Frequently asked questions

See all results for NVIDIA A2 16GBSee all hardware for Codestral 2 25.08
NVFP4
4
12.3 GB
Medium
F0
Q4_K_M
4
13.4 GB
MediumF0
Q5_K_M
5
15.8 GB
HighF0
Q6_K
6
18.0 GB
HighF0
Q8_0
8
23.5 GB
Very HighF0
F16
16
45.1 GB
MaximumF0

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.