RTX 3090 24GBGünstige Wahl24 GB VRAM (+4)936 GB/s (+296)
B
Raises estimated decode speed by about 31%.52.5 tok/s Dekodierung
Raises estimated decode speed by about 31%.
ca. $1,499 MSRP
Can Codestral 22B run on RTX A4500 20GB?
YES — With Offload
B61Good
Estimated from fit model
Codestral 22B needs ~19.1 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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) — 19.1 GB, 40.0 tok/s, Runs with offload
19.1 GB required20.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
40.0 tok/s
TTFT
4841 ms
Safe context
22K
Memory
19.1 GB / 20.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 22B on RTX A4500 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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 | 40.0 tok/s | 2641 ms | 22K |
| Coding | B | Runs with offload | 40.0 tok/s | 4841 ms | 22K |
| Agentic Coding | C | Runs with offload (needs ~0.9 GB host RAM) | 25.7 tok/s | 10936 ms | 22K |
| Reasoning | B | Runs with offload | 40.0 tok/s | 5722 ms | 22K |
| RAG | C | Runs with offload (needs ~0.9 GB host RAM) | 25.7 tok/s | 13671 ms | 22K |
Quantization options
How Codestral 22B (22B params) fits at each quantization level on RTX A4500 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 codestralUpgrade-Optionen
Hardware, die Codestral 22B gut ausführt
RTX 3090 24GBGünstige WahlRTX 4090 24GBBestes Preis-Leistungs-Verhältnis24 GB VRAM (+4)1008 GB/s (+368)
B
Raises estimated decode speed by about 53%.61.4 tok/s Dekodierung
Raises estimated decode speed by about 53%.
ca. $1,599 MSRP
RTX A5500 24GBNVIDIA-Upgrade24 GB VRAM (+4)768 GB/s (+128)
B
This setup is broadly balanced for this model.48 tok/s Dekodierung
ca. $3,200 MSRP
Frequently asked questions
Can RTX A4500 20GB run Codestral 22B?
Yes, RTX A4500 20GB can run Codestral 22B with a B grade (Runs with offload). Expected decode speed: 40.0 tok/s.
How much VRAM does Codestral 22B need?
Codestral 22B (22B parameters) requires approximately 19.1 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 RTX A4500 20GB?
On RTX A4500 20GB, Codestral 22B achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4841ms using Q4_K_M quantization.
Can RTX A4500 20GB run Codestral 22B for coding?
For coding workloads, Codestral 22B on RTX A4500 20GB receives a B grade with 40.0 tok/s and 22K context.
What context window can Codestral 22B use on RTX A4500 20GB?
On RTX A4500 20GB, Codestral 22B can safely use up to 22K 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 RTX A4500 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-rtx-a4500-20gb" 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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