RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1360)
C
Raises estimated decode speed by about 252%.103.6 tok/s 解码
Raises estimated decode speed by about 252%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Codestral RAG 19B Pruned i1 run on RTX 4500 Ada 24GB?
YES — Runs Great
C53Usable
Estimated from fit model
Codestral RAG 19B Pruned i1 needs ~17.4 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~29 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) — 17.4 GB, 29.4 tok/s, Runs well
17.4 GB required24.0 GB available
73% VRAM used
Fit status
Runs well
Decode
29.4 tok/s
TTFT
6575 ms
Safe context
63K
Memory
17.4 GB / 24.0 GB
Memory breakdown
Weights11.6 GB
KV Cache2.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsCodestral RAG 19B Pruned i1 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: 29.4 tok/s decode · 6.6s TTFT (warm) · 74 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 | 29.4 tok/s | 3586 ms | 63K |
| Coding | C | Runs well | 29.4 tok/s | 6575 ms | 63K |
| Agentic Coding | C | Runs well | 29.4 tok/s | 9563 ms | 63K |
| Reasoning | C | Runs well | 29.4 tok/s | 7770 ms | 63K |
| RAG | C | Runs well | 29.4 tok/s | 11954 ms | 63K |
Quantization options
How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | C47 |
Q3_K_S | 3 | 9.3 GB | Low | C48 |
NVFP4 | 4 | 10.6 GB | Medium | C49 |
Q4_K_M | 4 | 11.6 GB | Medium | C49 |
Q5_K_M | 5 | 13.7 GB | High | C50 |
Q6_KBest for your GPU | 6 | 15.6 GB | High | C49 |
Q8_0 | 8 | 20.3 GB | Very High | F0 |
F16 | 16 | 38.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run Codestral RAG 19B Pruned i1 on your machine.
Run
lms load hf-mradermacher--codestral-rag-19b-pruned-i1-gguf && lms server start升级选项
能流畅运行 Codestral RAG 19B Pruned i1 的硬件
Frequently asked questions
Can RTX 4500 Ada 24GB run Codestral RAG 19B Pruned i1?
Yes, RTX 4500 Ada 24GB can run Codestral RAG 19B Pruned i1 with a C grade (Runs well). Expected decode speed: 29.4 tok/s.
How much VRAM does Codestral RAG 19B Pruned i1 need?
Codestral RAG 19B Pruned i1 (19B parameters) requires approximately 17.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral RAG 19B Pruned i1?
The recommended quantization for Codestral RAG 19B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral RAG 19B Pruned i1 run at on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, Codestral RAG 19B Pruned i1 achieves approximately 29.4 tokens per second decode speed with a time-to-first-token of 6575ms using Q4_K_M quantization.
Can RTX 4500 Ada 24GB run Codestral RAG 19B Pruned i1 for coding?
For coding workloads, Codestral RAG 19B Pruned i1 on RTX 4500 Ada 24GB receives a C grade with 29.4 tok/s and 63K context.
What context window can Codestral RAG 19B Pruned i1 use on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, Codestral RAG 19B Pruned i1 can safely use up to 63K 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-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-rtx-4500-ada-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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