Can Codestral 2 25.08 run on RX 7800 XT 16GB?

Q: What should I upgrade first if Codestral 2 25.08 feels slow on RX 7800 XT 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. RX 7800 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~15 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, 16.4 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

16.4 tok/s

TTFT

11840 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 RX 7800 XT 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	17.6 tok/s	6006 ms	4K
Coding	A	Very compromised	15.2 tok/s	12727 ms	4K
Agentic Coding	F	Too heavy	11.7 tok/s	24077 ms	4K
Reasoning	A	Very compromised	15.2 tok/s	15042 ms	4K
RAG	F	Too heavy	11.7 tok/s	30096 ms	4K

Quantization options

How Codestral 2 25.08 (22B params) fits at each quantization level on RX 7800 XT 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 RX 7800 XT 16GB run Codestral 2 25.08?

Yes, RX 7800 XT 16GB can run Codestral 2 25.08 with a A grade (Very compromised). Expected decode speed: 15.2 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 RX 7800 XT 16GB?

On RX 7800 XT 16GB, Codestral 2 25.08 achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12727ms using Q4_K_M quantization.

Can RX 7800 XT 16GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on RX 7800 XT 16GB receives a A grade with 15.2 tok/s and 4K context.

What context window can Codestral 2 25.08 use on RX 7800 XT 16GB?

On RX 7800 XT 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 RX 7800 XT 16GB?

See all results for RX 7800 XT 16GB See all hardware for Codestral 2 25.08

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