32 GB VRAM (+8)1792 GB/s (+1024)
B
Raises estimated decode speed by about 101%.89.5 tok/s decode
Raises estimated decode speed by about 101%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Codestral 22B v0.1 IMat run on RTX A5500 24GB?
YES — Runs Great
C54Usable
○Estimated from fit model
Codestral 22B v0.1 IMat needs ~19.6 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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) — 19.6 GB, 44.6 tok/s, Runs well
19.6 GB required24.0 GB available
82% VRAM used
Fit status
Runs well
Decode
44.6 tok/s
TTFT
4337 ms
Safe context
43K
Memory
19.6 GB / 24.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 IMat on RTX A5500 24GB
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: 44.6 tok/s decode · 4.3s TTFT (warm) · 112 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 44.6 tok/s | 2366 ms | 43K |
| Coding | C | Runs well | 44.6 tok/s | 4337 ms | 43K |
| Agentic Coding | C | Tight fit | 44.6 tok/s | 6309 ms | 43K |
| Reasoning | C | Runs well | 44.6 tok/s | 5126 ms | 43K |
| RAG | C | Tight fit | 44.6 tok/s | 7886 ms | 43K |
Quantization options
How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C48 |
Q3_K_S | 3 | 10.8 GB | Low | C49 |
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 startUpgrade options