RX 7900 XTX 24GBGünstige Wahl
960 GB/s (+560)
C
Raises estimated decode speed by about 198%.125.9 tok/s Dekodierung
Raises estimated decode speed by about 198%.
ca. $999 MSRP
Can Yi Coder 9B Chat run on MacBook Pro M2 Max 32GB?
YES — Runs Great
C50Usable
Estimated from fit model
Yi Coder 9B Chat needs ~10.9 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~42 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) — 10.9 GB, 42.3 tok/s, Runs well
10.9 GB required23.0 GB available
47% VRAM used
Fit status
Runs well
Decode
42.3 tok/s
TTFT
4581 ms
Safe context
200K
Memory
10.9 GB / 23.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on MacBook Pro M2 Max 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: 42.3 tok/s decode · 4.6s TTFT (warm) · 106 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 | 42.3 tok/s | 2499 ms | 200K |
| Coding | C | Runs well | 42.3 tok/s | 4581 ms | 200K |
| Agentic Coding | C | Runs well | 42.3 tok/s | 6664 ms | 200K |
| Reasoning | C | Runs well | 42.3 tok/s | 5414 ms | 200K |
| RAG | C | Runs well | 42.3 tok/s | 8330 ms | 200K |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C47 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C50 |
Get started
Copy-paste commands to run Yi Coder 9B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server startUpgrade-Optionen
Hardware, die Yi Coder 9B Chat gut ausführt
Frequently asked questions
Can MacBook Pro M2 Max 32GB run Yi Coder 9B Chat?
Yes, MacBook Pro M2 Max 32GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 42.3 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B Chat?
The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B Chat run at on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Yi Coder 9B Chat achieves approximately 42.3 tokens per second decode speed with a time-to-first-token of 4581ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on MacBook Pro M2 Max 32GB receives a C grade with 42.3 tok/s and 200K context.
What context window can Yi Coder 9B Chat use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Yi Coder 9B Chat can safely use up to 200K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Yi Coder 9B Chat?
Not always. MacBook Pro M2 Max 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-m2-max-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: