Mac Studio M3 Ultra 96GBBudget pick
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.101.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Can Yi Coder 9B Chat run on NVIDIA L20 48GB?
YES — Runs Great
C48Usable
○Estimated from fit model
Yi Coder 9B Chat needs ~12.5 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~115 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Share:
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) — 12.5 GB, 114.9 tok/s, Runs well
12.5 GB required48.0 GB available
26% VRAM used
Fit status
Runs well
Decode
114.9 tok/s
TTFT
1685 ms
Safe context
554K
Memory
12.5 GB / 48.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on NVIDIA L20 48GB
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: 114.9 tok/s decode · 1.7s TTFT (warm) · 287 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 114.9 tok/s | 919 ms | 554K |
| Coding | C | Runs well | 114.9 tok/s | 1685 ms | 554K |
| Agentic Coding | C | Runs well | 114.9 tok/s | 2451 ms | 554K |
| Reasoning | C | Runs well | 114.9 tok/s | 1992 ms | 554K |
| RAG | C | Runs well | 114.9 tok/s | 3064 ms | 554K |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C42 |
Q3_K_S | 3 | 4.4 GB | Low | C42 |
NVFP4 | 4 |
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 options