Can Codestral 22B v0.1 IMat run on RX 7900 XT 20GB?

Q: What should I upgrade first if Codestral 22B v0.1 IMat feels slow on RX 7900 XT 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

C50Usable

Estimated from fit model

Codestral 22B v0.1 IMat needs ~18.9 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: Balanced

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) — 18.9 GB, 35.8 tok/s, Tight fit

18.9 GB required20.0 GB available

95% VRAM used

Fit status

Tight fit

Decode

35.8 tok/s

TTFT

5413 ms

Safe context

23K

Memory

18.9 GB / 20.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 IMat on RX 7900 XT 20GB
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: 35.8 tok/s decode · 5.4s TTFT (warm) · 89 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	35.8 tok/s	2952 ms	23K
Coding	C	Tight fit	35.8 tok/s	5413 ms	23K
Agentic Coding	D	Runs with offload (needs ~0.9 GB host RAM)	23.1 tok/s	12195 ms	23K
Reasoning	C	Tight fit	35.8 tok/s	6397 ms	23K
RAG	D	Runs with offload (needs ~0.9 GB host RAM)	23.1 tok/s	15244 ms

Quantization options

How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	C50
Q3_K_S	3	10.8 GB	Low	C50
NVFP4	4

Get started

Copy-paste commands to run Codestral 22B v0.1 IMat on your machine.

Run

lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server start

Upgrade options

Hardware that runs Codestral 22B v0.1 IMat well

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+4)960 GB/s (+160)

Raises estimated decode speed by about 44%.51.5 tok/s decode

Raises estimated decode speed by about 44%.

~$999 MSRP

Radeon AI PRO R9700 32GBBest value

32 GB VRAM (+12)

Adds memory headroom for longer context windows and future model growth.28.1 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,899 MSRP

Radeon Pro W7800 32GBAMD upgrade

32 GB VRAM (+12)

Adds memory headroom for longer context windows and future model growth.25.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run Codestral 22B v0.1 IMat?

Yes, RX 7900 XT 20GB can run Codestral 22B v0.1 IMat with a C grade (Tight fit). Expected decode speed: 35.8 tok/s.

How much VRAM does Codestral 22B v0.1 IMat need?

Codestral 22B v0.1 IMat (22B parameters) requires approximately 18.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1 IMat?

The recommended quantization for Codestral 22B v0.1 IMat is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 IMat run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Codestral 22B v0.1 IMat achieves approximately 35.8 tokens per second decode speed with a time-to-first-token of 5413ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Codestral 22B v0.1 IMat for coding?

For coding workloads, Codestral 22B v0.1 IMat on RX 7900 XT 20GB receives a C grade with 35.8 tok/s and 23K context.

What context window can Codestral 22B v0.1 IMat use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Codestral 22B v0.1 IMat can safely use up to 23K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Codestral 22B v0.1 IMat

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