RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1492)
B
Raises estimated decode speed by about 517%.89.5 tok/sデコード
Raises estimated decode speed by about 517%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Codestral 22B v0.1 IMat run on NVIDIA L4 24GB?
YES — Runs Great
C51Usable
Estimated from fit model
Codestral 22B v0.1 IMat needs ~19.6 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~15 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 19.6 GB, 14.5 tok/s, Runs well
19.6 GB required24.0 GB available
82% VRAM used
Fit status
Runs well
Decode
14.5 tok/s
TTFT
13324 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 NVIDIA L4 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: 14.5 tok/s decode · 13.3s TTFT (warm) · 36 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 | 14.5 tok/s | 7267 ms | 43K |
| Coding | C | Runs well | 14.5 tok/s | 13324 ms | 43K |
| Agentic Coding | C | Tight fit | 14.5 tok/s | 19380 ms | 43K |
| Reasoning | C | Runs well | 14.5 tok/s | 15746 ms | 43K |
| RAG | C | Tight fit | 14.5 tok/s | 24225 ms | 43K |
Quantization options
How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on NVIDIA L4 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 | 12.3 GB | Medium | C50 |
Q4_K_M | 4 | 13.4 GB | Medium | C50 |
Q5_K_M | 5 | 15.8 GB | High | C49 |
Q6_KBest for your GPU | 6 | 18.0 GB | High | C49 |
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 startアップグレードオプション
Codestral 22B v0.1 IMatを快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 287%.56.1 tok/sデコード
Raises estimated decode speed by about 287%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 137%.34.3 tok/sデコード
Raises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run Codestral 22B v0.1 IMat?
Yes, NVIDIA L4 24GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 14.5 tok/s.
How much VRAM does Codestral 22B v0.1 IMat need?
Codestral 22B v0.1 IMat (22B parameters) requires approximately 19.6 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 NVIDIA L4 24GB?
On NVIDIA L4 24GB, Codestral 22B v0.1 IMat achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13324ms using Q4_K_M quantization.
Can NVIDIA L4 24GB run Codestral 22B v0.1 IMat for coding?
For coding workloads, Codestral 22B v0.1 IMat on NVIDIA L4 24GB receives a C grade with 14.5 tok/s and 43K context.
What context window can Codestral 22B v0.1 IMat use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, Codestral 22B v0.1 IMat can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-legraphista--codestral-22b-v0-1-imat-gguf-on-l4-24gb" 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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