RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1360)
B
Raises estimated decode speed by about 252%.89.5 tok/sデコード
Raises estimated decode speed by about 252%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Codestral 22B v0.1 run on RTX 4500 Ada 24GB?
YES — Runs Great
C53Usable
Estimated from fit model
Codestral 22B v0.1 needs ~19.6 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~25 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, 25.4 tok/s, Runs well
19.6 GB required24.0 GB available
82% VRAM used
Fit status
Runs well
Decode
25.4 tok/s
TTFT
7613 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 on RTX 4500 Ada 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: 25.4 tok/s decode · 7.6s TTFT (warm) · 64 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 | 25.4 tok/s | 4152 ms | 43K |
| Coding | C | Runs well | 25.4 tok/s | 7613 ms | 43K |
| Agentic Coding | C | Tight fit | 25.4 tok/s | 11073 ms | 43K |
| Reasoning | C | Runs well | 25.4 tok/s | 8997 ms | 43K |
| RAG | C | Tight fit | 25.4 tok/s | 13842 ms | 43K |
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX 4500 Ada 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 | C50 |
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 on your machine.
Run
lms load hf-bartowski--codestral-22b-v0-1-gguf && lms server startアップグレードオプション
Codestral 22B v0.1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 (+464)
C
Raises estimated decode speed by about 121%.56.1 tok/sデコード
Raises estimated decode speed by about 121%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
NVIDIA V100 32GBNVIDIAアップグレード32 GB VRAM (+8)900 GB/s (+468)
C
Raises estimated decode speed by about 77%.44.9 tok/sデコード
Raises estimated decode speed by about 77%.
Adds memory headroom for longer context windows and future model growth.
〜$8,999 MSRP
Frequently asked questions
Can RTX 4500 Ada 24GB run Codestral 22B v0.1?
Yes, RTX 4500 Ada 24GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 25.4 tok/s.
How much VRAM does Codestral 22B v0.1 need?
Codestral 22B v0.1 (22B parameters) requires approximately 19.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 22B v0.1?
The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 22B v0.1 run at on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, Codestral 22B v0.1 achieves approximately 25.4 tokens per second decode speed with a time-to-first-token of 7613ms using Q4_K_M quantization.
Can RTX 4500 Ada 24GB run Codestral 22B v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on RTX 4500 Ada 24GB receives a C grade with 25.4 tok/s and 43K context.
What context window can Codestral 22B v0.1 use on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, Codestral 22B v0.1 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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