RTX 5090 32GBOpção econômica1792 GB/s (+1382)
C
Raises estimated decode speed by about 196%.82.8 tok/s decodificação
Raises estimated decode speed by about 196%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Codestral 21B Pruned i1 run on MacBook Pro M4 Max 36GB?
YES — Runs Great
C53Usable
Estimated from fit model
Codestral 21B Pruned i1 needs ~20.1 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 20.1 GB, 28.0 tok/s, Runs well
20.1 GB required25.9 GB available
78% VRAM used
Fit status
Runs well
Decode
28.0 tok/s
TTFT
6905 ms
Safe context
54K
Memory
20.1 GB / 25.9 GB
Memory breakdown
Weights12.8 GB
KV Cache2.5 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsCodestral 21B Pruned i1 on MacBook Pro M4 Max 36GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 28.0 tok/s | 3766 ms | 54K |
| Coding | C | Runs well | 28.0 tok/s | 6905 ms | 54K |
| Agentic Coding | C | Tight fit | 28.0 tok/s | 10044 ms | 54K |
| Reasoning | C | Runs well | 28.0 tok/s | 8160 ms | 54K |
| RAG | C | Tight fit | 28.0 tok/s | 12554 ms | 54K |
Quantization options
How Codestral 21B Pruned i1 (21B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 8.2 GB | Low | C46 |
Q3_K_S | 3 | 10.3 GB | Low | C48 |
NVFP4 | 4 | 11.8 GB | Medium | C49 |
Q4_K_M | 4 | 12.8 GB | Medium | C49 |
Q5_K_M | 5 | 15.1 GB | High | C49 |
Q6_KBest for your GPU | 6 | 17.2 GB | High | C49 |
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
Frequently asked questions
Can MacBook Pro M4 Max 36GB run Codestral 21B Pruned i1?
Yes, MacBook Pro M4 Max 36GB can run Codestral 21B Pruned i1 with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
How much VRAM does Codestral 21B Pruned i1 need?
Codestral 21B Pruned i1 (21B parameters) requires approximately 20.1 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 MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 36GB, Codestral 21B Pruned i1 achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6905ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 36GB run Codestral 21B Pruned i1 for coding?
For coding workloads, Codestral 21B Pruned i1 on MacBook Pro M4 Max 36GB receives a C grade with 28.0 tok/s and 54K context.
What context window can Codestral 21B Pruned i1 use on MacBook Pro M4 Max 36GB?
On MacBook Pro M4 Max 36GB, Codestral 21B Pruned i1 can safely use up to 54K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for Codestral 21B Pruned i1?
Not always. MacBook Pro M4 Max 36GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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