RX 7900 XTX 24GBOpción económica
24 GB VRAM (+4)960 GB/s (+160)
B
Sube la velocidad estimada de decodificación alrededor de un 44%.55.4 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 44%.
~$999 MSRP
Can Codestral 22B run on RX 7900 XT 20GB?
YES — Tight Fit
B61Good
Estimated from fit model
Codestral 22B needs ~18.8 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~39 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.
Select quantization to explore
Q4_K_M (Medium quality) — 18.8 GB, 38.5 tok/s, Tight fit
18.8 GB required20.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
38.5 tok/s
TTFT
5035 ms
Safe context
24K
Memory
18.8 GB / 20.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 22B 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: 38.5 tok/s decode · 5.0s TTFT (warm) · 96 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 | B | Tight fit | 38.5 tok/s | 2746 ms | 24K |
| Coding | B | Tight fit | 38.5 tok/s | 5035 ms | 24K |
| Agentic Coding | C | Runs with offload (needs ~0.8 GB host RAM) | 25.5 tok/s | 11042 ms | 24K |
| Reasoning | B | Tight fit | 38.5 tok/s | 5951 ms | 24K |
| RAG | C | Runs with offload (needs ~0.8 GB host RAM) | 25.5 tok/s | 13803 ms | 24K |
Quantization options
How Codestral 22B (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 | B60 |
Q3_K_S | 3 | 10.8 GB | Low | B61 |
NVFP4 | 4 | 12.3 GB | Medium | B60 |
Q4_K_MBest for your GPU | 4 | 13.4 GB | Medium | B60 |
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 22B on your machine.
Run
ollama run codestralOpciones de mejora
Hardware que ejecuta bien Codestral 22B
Radeon AI PRO R9700 32GBMejor relación calidad-precio
32 GB VRAM (+12)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.30.2 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,899 MSRP
Radeon Pro W7800 32GBMejora AMD
32 GB VRAM (+12)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.27.2 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Frequently asked questions
Can RX 7900 XT 20GB run Codestral 22B?
Yes, RX 7900 XT 20GB can run Codestral 22B with a B grade (Tight fit). Expected decode speed: 38.5 tok/s.
How much VRAM does Codestral 22B need?
Codestral 22B (22B parameters) requires approximately 18.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 22B?
The recommended quantization for Codestral 22B is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 22B run at on RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 22B achieves approximately 38.5 tokens per second decode speed with a time-to-first-token of 5035ms using Q4_K_M quantization.
Can RX 7900 XT 20GB run Codestral 22B for coding?
For coding workloads, Codestral 22B on RX 7900 XT 20GB receives a B grade with 38.5 tok/s and 24K context.
What context window can Codestral 22B use on RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 22B can safely use up to 24K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
What should I upgrade first if Codestral 22B 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.
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<iframe src="https://willitrunai.com/embed/codestral-22b-on-rx-7900-xt-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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