Can Codestral 2 25.08 run on NVIDIA A2 16GB?

BARELY — Tight on Memory

B70Good

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

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

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 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)	7.2 tok/s	14586 ms	4K
Coding	B	Very compromised (needs ~1.7 GB host RAM)	6.3 tok/s	30907 ms	4K
Agentic Coding	F	Too heavy	4.8 tok/s	58467 ms	4K
Reasoning	B	Very compromised (needs ~1.7 GB host RAM)	6.3 tok/s	36527 ms	4K
RAG	F	Too heavy	4.8 tok/s	73084 ms	4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on NVIDIA A2 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
NVFP4	4	12.3 GB	Medium	F0
Q4_K_M	4	13.4 GB	Medium	F0
Q5_K_M	5	15.8 GB	High	F0
Q6_K	6	18.0 GB	High	F0
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

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

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

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

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+736)

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

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

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

Can NVIDIA A2 16GB run Codestral 2 25.08?

Yes, NVIDIA A2 16GB can run Codestral 2 25.08 with a B grade (Very compromised (needs ~1.7 GB host RAM)). Expected decode speed: 6.3 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Codestral 2 25.08 achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30907ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on NVIDIA A2 16GB receives a B grade with 6.3 tok/s and 4K context.

What context window can Codestral 2 25.08 use on NVIDIA A2 16GB?

On NVIDIA A2 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.

What should I upgrade first if Codestral 2 25.08 feels slow on NVIDIA A2 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.

See all results for NVIDIA A2 16GB See all hardware for Codestral 2 25.08

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Preview:

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)	7.2 tok/s	14586 ms	4K
Coding	B	Very compromised (needs ~1.7 GB host RAM)	6.3 tok/s	30907 ms	4K
Agentic Coding	F	Too heavy	4.8 tok/s	58467 ms	4K
Reasoning	B	Very compromised (needs ~1.7 GB host RAM)	6.3 tok/s	36527 ms	4K
RAG	F	Too heavy	4.8 tok/s	73084 ms	4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on NVIDIA A2 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
NVFP4	4	12.3 GB	Medium	F0
Q4_K_M	4	13.4 GB	Medium	F0
Q5_K_M	5	15.8 GB	High	F0
Q6_K	6	18.0 GB	High	F0
Q8_0	8	23.5 GB	Very High	F0
F16	16	45.1 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A2 16GB run Codestral 2 25.08?

Yes, NVIDIA A2 16GB can run Codestral 2 25.08 with a B grade (Very compromised (needs ~1.7 GB host RAM)). Expected decode speed: 6.3 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Codestral 2 25.08 achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30907ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on NVIDIA A2 16GB receives a B grade with 6.3 tok/s and 4K context.

What context window can Codestral 2 25.08 use on NVIDIA A2 16GB?

On NVIDIA A2 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.

What should I upgrade first if Codestral 2 25.08 feels slow on NVIDIA A2 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.