MacBook Pro M4 Max 36GBGünstige Wahl
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 117%.65.9 tok/s Dekodierung
Raises estimated decode speed by about 117%.
ca. $2,499 MSRP
Can CodeNinja 1.0 OpenChat 7B i1 run on MacBook Pro M1 Pro 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
CodeNinja 1.0 OpenChat 7B i1 needs ~9.4 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 9.4 GB, 30.4 tok/s, Runs well
9.4 GB required23.0 GB available
41% VRAM used
Fit status
Runs well
Decode
30.4 tok/s
TTFT
6359 ms
Safe context
281K
Memory
9.4 GB / 23.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M1 Pro 32GB
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: 30.4 tok/s decode · 6.4s TTFT (warm) · 76 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 | 30.4 tok/s | 3469 ms | 281K |
| Coding | C | Runs well | 30.4 tok/s | 6359 ms | 281K |
| Agentic Coding | C | Runs well | 30.4 tok/s | 9249 ms | 281K |
| Reasoning | C | Runs well | 30.4 tok/s | 7515 ms | 281K |
| RAG | C | Runs well | 30.4 tok/s | 11562 ms | 281K |
Quantization options
How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C44 |
Q3_K_S | 3 | 3.4 GB | Low | C44 |
NVFP4 | 4 | 3.9 GB | Medium | C45 |
Q4_K_M | 4 | 4.3 GB | Medium | C45 |
Q5_K_M | 5 | 5.0 GB | High | C45 |
Q6_K | 6 | 5.7 GB | High | C46 |
Q8_0 | 8 | 7.5 GB | Very High | C47 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C50 |
Get started
Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.
Run
lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server startUpgrade-Optionen
Hardware, die CodeNinja 1.0 OpenChat 7B i1 gut ausführt
MacBook Pro M4 Max 48GBBestes Preis-Leistungs-Verhältnis
48 GB Unified (+16)546 GB/s (+346)
C
Raises estimated decode speed by about 189%.87.8 tok/s Dekodierung
Raises estimated decode speed by about 189%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
MacBook Pro M3 Max 48GBApple-Upgrade
48 GB Unified (+16)400 GB/s (+200)
C
Raises estimated decode speed by about 85%.56.2 tok/s Dekodierung
Raises estimated decode speed by about 85%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Frequently asked questions
Can MacBook Pro M1 Pro 32GB run CodeNinja 1.0 OpenChat 7B i1?
Yes, MacBook Pro M1 Pro 32GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 30.4 tok/s.
How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?
CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.
What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?
The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will CodeNinja 1.0 OpenChat 7B i1 run at on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 30.4 tokens per second decode speed with a time-to-first-token of 6359ms using Q4_K_M quantization.
Can MacBook Pro M1 Pro 32GB run CodeNinja 1.0 OpenChat 7B i1 for coding?
For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on MacBook Pro M1 Pro 32GB receives a C grade with 30.4 tok/s and 281K context.
What context window can CodeNinja 1.0 OpenChat 7B i1 use on MacBook Pro M1 Pro 32GB?
On MacBook Pro M1 Pro 32GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 281K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for CodeNinja 1.0 OpenChat 7B i1?
Not always. MacBook Pro M1 Pro 32GB 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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