RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+896)
B
Raises estimated decode speed by about 60%.89.5 tok/sデコード
Raises estimated decode speed by about 60%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Codestral 22B v0.1 run on RTX 5090 Laptop 24GB?
YES — Runs Great
B55Good
Estimated from fit model
Codestral 22B v0.1 needs ~19.6 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~56 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, 56.1 tok/s, Runs well
19.6 GB required24.0 GB available
82% VRAM used
Fit status
Runs well
Decode
56.1 tok/s
TTFT
3452 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 5090 Laptop 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: 56.1 tok/s decode · 3.5s TTFT (warm) · 140 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 | B | Runs well | 56.1 tok/s | 1883 ms | 43K |
| Coding | B | Runs well | 56.1 tok/s | 3452 ms | 43K |
| Agentic Coding | C | Tight fit | 56.1 tok/s | 5021 ms | 43K |
| Reasoning | B | Runs well | 56.1 tok/s | 4080 ms | 43K |
| RAG | C | Tight fit | 56.1 tok/s | 6276 ms | 43K |
Inference speed
Codestral 22B v0.1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Codestral 22B v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~90 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 89.5 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 57.1 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 51.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 48.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 41.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.8 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 34.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 32.8 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 24.3 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.9 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 17.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 16.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 8.6 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 5.4 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on RTX 5090 Laptop 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-sanctumai--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
Frequently asked questions
Can RTX 5090 Laptop 24GB run Codestral 22B v0.1?
Yes, RTX 5090 Laptop 24GB can run Codestral 22B v0.1 with a B grade (Runs well). Expected decode speed: 56.1 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 5090 Laptop 24GB?
On RTX 5090 Laptop 24GB, Codestral 22B v0.1 achieves approximately 56.1 tokens per second decode speed with a time-to-first-token of 3452ms using Q4_K_M quantization.
Can RTX 5090 Laptop 24GB run Codestral 22B v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on RTX 5090 Laptop 24GB receives a B grade with 56.1 tok/s and 43K context.
What context window can Codestral 22B v0.1 use on RTX 5090 Laptop 24GB?
On RTX 5090 Laptop 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-sanctumai--codestral-22b-v0-1-gguf-on-rtx-5090-laptop-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: