24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.48.8 tok/s decode
Raises estimated decode speed by about 133%.
~$1,499 MSRP
Can Codestral 22B v0.1 IMat run on RTX 4000 Ada 20GB?
YES — With Offload
C49Usable
○Estimated from fit model
Codestral 22B v0.1 IMat needs ~19.2 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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) — 19.2 GB, 20.9 tok/s, Runs with offload
19.2 GB required20.0 GB available
96% VRAM used
Fit status
Runs with offload
Decode
20.9 tok/s
TTFT
9253 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 4000 Ada 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: 20.9 tok/s decode · 9.3s TTFT (warm) · 52 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 | 20.9 tok/s | 5047 ms | 21K |
| Coding | C | Runs with offload | 20.9 tok/s | 9253 ms | 21K |
| Agentic Coding | D | Very compromised (needs ~1.1 GB host RAM) | 13.1 tok/s | 21464 ms | 21K |
| Reasoning | C | Runs with offload | 20.9 tok/s | 10935 ms | 21K |
| RAG | D | Very compromised (needs ~1.1 GB host RAM) | 13.1 tok/s | 26830 ms | 21K |
Quantization options
How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 4000 Ada 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 startUpgrade options