RTX 3090 24GBOpção econômica24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.51.1 tok/s decodificação
Raises estimated decode speed by about 133%.
~$1,499 MSRP
Can Codestral 21B Pruned i1 run on RTX 4000 Ada 20GB?
YES — Tight Fit
C49Usable
Estimated from fit model
Codestral 21B Pruned i1 needs ~18.5 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 18.5 GB, 21.9 tok/s, Tight fit
18.5 GB required20.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
21.9 tok/s
TTFT
8832 ms
Safe context
26K
Memory
18.5 GB / 20.0 GB
Memory breakdown
Weights12.8 GB
KV Cache2.5 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 21B Pruned i1 on RTX 4000 Ada 20GB
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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 21.9 tok/s | 4817 ms | 26K |
| Coding | C | Tight fit | 21.9 tok/s | 8832 ms | 26K |
| Agentic Coding | C | Runs with offload (needs ~0.6 GB host RAM) | 14.9 tok/s | 18852 ms | 26K |
| Reasoning | C | Tight fit | 21.9 tok/s | 10438 ms | 26K |
| RAG | C | Runs with offload (needs ~0.6 GB host RAM) | 14.9 tok/s | 23565 ms | 26K |
Quantization options
How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | C49 |
Q3_K_S | 3 | 10.3 GB | Low | C50 |
NVFP4 | 4 | 11.8 GB | Medium | C50 |
Q4_K_M | 4 | 12.8 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 15.1 GB | High | C49 |
Q6_K | 6 | 17.2 GB | High | F0 |
Q8_0 | 8 | 22.5 GB | Very High | F0 |
F16 | 16 | 43.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run Codestral 21B Pruned i1 on your machine.
Run
lms load hf-mradermacher--codestral-21b-pruned-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem Codestral 21B Pruned i1
RTX 3090 24GBOpção econômicaRTX 4090 24GBMelhor custo-benefício24 GB VRAM (+4)1008 GB/s (+648)
B
Raises estimated decode speed by about 173%.59.8 tok/s decodificação
Raises estimated decode speed by about 173%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBUpgrade NVIDIA24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 101%.44.1 tok/s decodificação
Raises estimated decode speed by about 101%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run Codestral 21B Pruned i1?
Yes, RTX 4000 Ada 20GB can run Codestral 21B Pruned i1 with a C grade (Tight fit). Expected decode speed: 21.9 tok/s.
How much VRAM does Codestral 21B Pruned i1 need?
Codestral 21B Pruned i1 (21B parameters) requires approximately 18.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Codestral 21B Pruned i1?
The recommended quantization for Codestral 21B Pruned i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Codestral 21B Pruned i1 run at on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Codestral 21B Pruned i1 achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8832ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run Codestral 21B Pruned i1 for coding?
For coding workloads, Codestral 21B Pruned i1 on RTX 4000 Ada 20GB receives a C grade with 21.9 tok/s and 26K context.
What context window can Codestral 21B Pruned i1 use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Codestral 21B Pruned i1 can safely use up to 26K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Codestral 21B Pruned i1 feels slow on RTX 4000 Ada 20GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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