RX 7900 XTX 24GBOpção econômica
24 GB VRAM (+4)960 GB/s (+160)
C
Raises estimated decode speed by about 44%.51.5 tok/s decodificação
Raises estimated decode speed by about 44%.
~$999 MSRP
Can Codestral 22B v0.1 run on RX 7900 XT 20GB?
YES — Tight Fit
C51Usable
Estimated from fit model
Codestral 22B v0.1 needs ~18.9 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~36 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) — 18.9 GB, 35.8 tok/s, Tight fit
18.9 GB required20.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
35.8 tok/s
TTFT
5413 ms
Safe context
23K
Memory
18.9 GB / 20.0 GB
Memory breakdown
Weights13.4 GB
KV Cache2.6 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsCodestral 22B v0.1 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: 35.8 tok/s decode · 5.4s TTFT (warm) · 89 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 | 35.8 tok/s | 2952 ms | 23K |
| Coding | C | Tight fit | 35.8 tok/s | 5413 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 23.1 tok/s | 12195 ms | 23K |
| Reasoning | C | Tight fit | 35.8 tok/s | 6397 ms | 23K |
| RAG | D | Runs with offload (needs ~0.9 GB host RAM) | 23.1 tok/s | 15244 ms | 23K |
Quantization options
How Codestral 22B v0.1 (22B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.6 GB | Low | C50 |
Q3_K_S | 3 | 10.8 GB | Low | C50 |
NVFP4 | 4 | 12.3 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 13.4 GB | Medium | C50 |
Q5_K_M | 5 | 15.8 GB | High | F0 |
Q6_K | 6 | 18.0 GB | High | F0 |
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 startOpções de upgrade
Hardware que roda bem Codestral 22B v0.1
Radeon AI PRO R9700 32GBMelhor custo-benefício
32 GB VRAM (+12)
C
Adds memory headroom for longer context windows and future model growth.28.1 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Radeon Pro W7800 32GBUpgrade AMD
32 GB VRAM (+12)
C
Adds memory headroom for longer context windows and future model growth.25.3 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Frequently asked questions
Can RX 7900 XT 20GB run Codestral 22B v0.1?
Yes, RX 7900 XT 20GB can run Codestral 22B v0.1 with a C grade (Tight fit). Expected decode speed: 35.8 tok/s.
How much VRAM does Codestral 22B v0.1 need?
Codestral 22B v0.1 (22B parameters) requires approximately 18.9 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 RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 22B v0.1 achieves approximately 35.8 tokens per second decode speed with a time-to-first-token of 5413ms using Q4_K_M quantization.
Can RX 7900 XT 20GB run Codestral 22B v0.1 for coding?
For coding workloads, Codestral 22B v0.1 on RX 7900 XT 20GB receives a C grade with 35.8 tok/s and 23K context.
What context window can Codestral 22B v0.1 use on RX 7900 XT 20GB?
On RX 7900 XT 20GB, Codestral 22B v0.1 can safely use up to 23K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Codestral 22B v0.1 feels slow on RX 7900 XT 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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