RX 7900 XT 20GBGünstige Wahl
800 GB/s (+527)
C
Raises estimated decode speed by about 148%.87.4 tok/s Dekodierung
Raises estimated decode speed by about 148%.
ca. $899 MSRP
Can Yi 9B Coder i1 run on MacBook Pro M4 Pro 24GB?
YES — Runs Great
C51Usable
Estimated — low-sample bucket· few comparable runs
Yi 9B Coder i1 needs ~10.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~35 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) — 10.0 GB, 35.2 tok/s, Runs well
10.0 GB required17.3 GB available
58% VRAM used
Fit status
Runs well
Decode
35.2 tok/s
TTFT
5496 ms
Safe context
126K
Memory
10.0 GB / 17.3 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 on MacBook Pro M4 Pro 24GB
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: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 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 | 35.2 tok/s | 2998 ms | 126K |
| Coding | C | Runs well | 35.2 tok/s | 5496 ms | 126K |
| Agentic Coding | C | Runs well | 35.2 tok/s | 7994 ms | 126K |
| Reasoning | C | Runs well | 35.2 tok/s | 6495 ms | 126K |
| RAG | C | Runs well | 35.2 tok/s | 9992 ms | 126K |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C46 |
Q3_K_S | 3 | 4.4 GB | Low | C47 |
NVFP4 | 4 | 5.0 GB | Medium | C48 |
Q4_K_M | 4 | 5.5 GB | Medium | C48 |
Q5_K_M | 5 | 6.5 GB | High | C49 |
Q6_K | 6 | 7.4 GB | High | C50 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi 9B Coder i1 on your machine.
Run
lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server startUpgrade-Optionen
Hardware, die Yi 9B Coder i1 gut ausführt
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run Yi 9B Coder i1?
Yes, MacBook Pro M4 Pro 24GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 35.2 tok/s.
How much VRAM does Yi 9B Coder i1 need?
Yi 9B Coder i1 (9B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 9B Coder i1?
The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 9B Coder i1 run at on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, Yi 9B Coder i1 achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5496ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on MacBook Pro M4 Pro 24GB receives a C grade with 35.2 tok/s and 126K context.
What context window can Yi 9B Coder i1 use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, Yi 9B Coder i1 can safely use up to 126K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for Yi 9B Coder i1?
Not always. MacBook Pro M4 Pro 24GB 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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