RX 7900 XTX 24GBGünstige Wahl
24 GB VRAM (+4)960 GB/s (+160)
C
Raises estimated decode speed by about 44%.54 tok/s Dekodierung
Raises estimated decode speed by about 44%.
ca. $999 MSRP
Can Codestral 21B Pruned i1 run on RX 7900 XT 20GB?
YES — Tight Fit
C50Usable
Estimated from fit model
Codestral 21B Pruned i1 needs ~18.2 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 18.2 GB, 37.5 tok/s, Tight fit
18.2 GB required20.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
37.5 tok/s
TTFT
5167 ms
Safe context
28K
Memory
18.2 GB / 20.0 GB
Memory breakdown
Weights12.8 GB
KV Cache2.5 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 21B Pruned i1 on RX 7900 XT 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: 37.5 tok/s decode · 5.2s TTFT (warm) · 94 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 | Tight fit | 37.5 tok/s | 2818 ms | 28K |
| Coding | C | Tight fit | 37.5 tok/s | 5167 ms | 28K |
| Agentic Coding | C | Runs with offload (needs ~0.4 GB host RAM) | 26.3 tok/s | 10698 ms | 28K |
| Reasoning | C | Tight fit | 37.5 tok/s | 6106 ms | 28K |
| RAG | C | Runs with offload (needs ~0.4 GB host RAM) | 26.3 tok/s | 13373 ms | 28K |
Quantization options
How Codestral 21B Pruned i1 (21B params) fits at each quantization level on RX 7900 XT 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 startUpgrade-Optionen
Hardware, die Codestral 21B Pruned i1 gut ausführt
Radeon AI PRO R9700 32GBBestes Preis-Leistungs-Verhältnis
32 GB VRAM (+12)
C
Adds memory headroom for longer context windows and future model growth.29.5 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,899 MSRP
AMD Instinct MI60 32GBAMD-Upgrade
32 GB VRAM (+12)1024 GB/s (+224)
C
Adds memory headroom for longer context windows and future model growth.39.2 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $8,999 MSRP
Frequently asked questions
Can RX 7900 XT 20GB run Codestral 21B Pruned i1?
Yes, RX 7900 XT 20GB can run Codestral 21B Pruned i1 with a C grade (Tight fit). Expected decode speed: 37.5 tok/s.
How much VRAM does Codestral 21B Pruned i1 need?
Codestral 21B Pruned i1 (21B parameters) requires approximately 18.2 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 RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 21B Pruned i1 achieves approximately 37.5 tokens per second decode speed with a time-to-first-token of 5167ms using Q4_K_M quantization.
Can RX 7900 XT 20GB run Codestral 21B Pruned i1 for coding?
For coding workloads, Codestral 21B Pruned i1 on RX 7900 XT 20GB receives a C grade with 37.5 tok/s and 28K context.
What context window can Codestral 21B Pruned i1 use on RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 21B Pruned i1 can safely use up to 28K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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