RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+856)
B
Raises estimated decode speed by about 83%.89.5 tok/s 解码
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Codestral 22B v0.1 IMat run on RTX 3090 24GB?
YES — Runs Great
C54Usable
Estimated from fit model
Codestral 22B v0.1 IMat needs ~19.6 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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, 48.8 tok/s, Runs well
19.6 GB required24.0 GB available
82% VRAM used
Fit status
Runs well
Decode
48.8 tok/s
TTFT
3965 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 3090 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: 48.8 tok/s decode · 4.0s TTFT (warm) · 122 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 | 48.8 tok/s | 2163 ms | 43K |
| Coding | C | Runs well | 48.8 tok/s | 3965 ms | 43K |
| Agentic Coding | C | Tight fit | 48.8 tok/s | 5768 ms | 43K |
| Reasoning | C | Runs well | 48.8 tok/s | 4686 ms | 43K |
| RAG | C | Tight fit | 48.8 tok/s | 7210 ms | 43K |
Quantization options
How Codestral 22B v0.1 IMat (22B params) fits at each quantization level on RTX 3090 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升级选项
能流畅运行 Codestral 22B v0.1 IMat 的硬件
Frequently asked questions
Can RTX 3090 24GB run Codestral 22B v0.1 IMat?
Yes, RTX 3090 24GB can run Codestral 22B v0.1 IMat with a C grade (Runs well). Expected decode speed: 48.8 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 RTX 3090 24GB?
On RTX 3090 24GB, Codestral 22B v0.1 IMat achieves approximately 48.8 tokens per second decode speed with a time-to-first-token of 3965ms using Q4_K_M quantization.
Can RTX 3090 24GB run Codestral 22B v0.1 IMat for coding?
For coding workloads, Codestral 22B v0.1 IMat on RTX 3090 24GB receives a C grade with 48.8 tok/s and 43K context.
What context window can Codestral 22B v0.1 IMat use on RTX 3090 24GB?
On RTX 3090 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-rtx-3090-24gb" 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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