RTX 3090 24GBOpção econômica24 GB VRAM (+4)936 GB/s (+296)
C
Raises estimated decode speed by about 31%.48.8 tok/s decodificação
Raises estimated decode speed by about 31%.
~$1,499 MSRP
Can Codestral 22B v0.1 IMat run on RTX A4500 20GB?
YES — With Offload
C51Usable
Estimated from fit model
Codestral 22B v0.1 IMat needs ~19.2 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~37 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) — 19.2 GB, 37.2 tok/s, Runs with offload
19.2 GB required20.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
37.2 tok/s
TTFT
5205 ms
Safe context
21K
Memory
19.2 GB / 20.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 IMat 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: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 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 | 37.2 tok/s | 2839 ms | 21K |
| Coding | C | Runs with offload | 37.2 tok/s | 5205 ms | 21K |
| Agentic Coding | D | Very compromised (needs ~1.1 GB host RAM) | 23.3 tok/s | 12074 ms | 21K |
| Reasoning | C | Runs with offload | 37.2 tok/s | 6151 ms | 21K |
| RAG | D | Very compromised (needs ~1.1 GB host RAM) | 23.3 tok/s | 15092 ms | 21K |
Quantization options
How Codestral 22B v0.1 IMat (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 | C50 |
Q3_K_S | 3 | 10.8 GB | Low | C50 |
NVFP4 | 4 | 12.3 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 13.4 GB | Medium | C50 |
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 v0.1 IMat on your machine.
Run
lms load hf-legraphista--codestral-22b-v0-1-imat-gguf && lms server startOpções de upgrade
Hardware que roda bem Codestral 22B v0.1 IMat
RTX 3090 24GBOpção econômicaRTX 4090 24GBMelhor custo-benefício24 GB VRAM (+4)1008 GB/s (+368)
C
Raises estimated decode speed by about 53%.57.1 tok/s decodificação
Raises estimated decode speed by about 53%.
~$1,599 MSRP
RTX A5500 24GBUpgrade NVIDIA24 GB VRAM (+4)768 GB/s (+128)
C
This setup is broadly balanced for this model.44.6 tok/s decodificação
~$3,200 MSRP
Frequently asked questions
Can RTX A4500 20GB run Codestral 22B v0.1 IMat?
Yes, RTX A4500 20GB can run Codestral 22B v0.1 IMat with a C grade (Runs with offload). Expected decode speed: 37.2 tok/s.
How much VRAM does Codestral 22B v0.1 IMat need?
Codestral 22B v0.1 IMat (22B parameters) requires approximately 19.2 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 RTX A4500 20GB?
On RTX A4500 20GB, Codestral 22B v0.1 IMat achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5205ms using Q4_K_M quantization.
Can RTX A4500 20GB run Codestral 22B v0.1 IMat for coding?
For coding workloads, Codestral 22B v0.1 IMat on RTX A4500 20GB receives a C grade with 37.2 tok/s and 21K context.
What context window can Codestral 22B v0.1 IMat use on RTX A4500 20GB?
On RTX A4500 20GB, Codestral 22B v0.1 IMat can safely use up to 21K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Codestral 22B v0.1 IMat 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-legraphista--codestral-22b-v0-1-imat-gguf-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>
Preview: