Mac Studio M3 Ultra 256GBOpción económica
256 GB Unified (+76)
B
Este setup está bastante equilibrado para este modelo.110.3 tok/s decodificación
~$6,999 MSRP
Can Yi Coder 9B run on NVIDIA B200 180GB?
YES — Runs Great
B57Good
Estimated from fit model
Yi Coder 9B needs ~26.2 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~126 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 26.2 GB, 126.0 tok/s, Runs well
26.2 GB required180.0 GB available
15% VRAM used
Fit status
Runs well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
131K
Memory
26.2 GB / 180.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsYi Coder 9B on NVIDIA B200 180GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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 | B | Runs well | 126.0 tok/s | 838 ms | 131K |
| Coding | B | Runs well | 126.0 tok/s | 1537 ms | 131K |
| Agentic Coding | B | Runs well | 126.0 tok/s | 2235 ms | 131K |
| Reasoning | B | Runs well | 126.0 tok/s | 1816 ms | 131K |
| RAG | B | Runs well | 126.0 tok/s | 2794 ms | 131K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C49 |
Q3_K_S | 3 | 4.4 GB | Low | C49 |
NVFP4 | 4 | 5.0 GB | Medium | C49 |
Q4_K_M | 4 | 5.5 GB | Medium | C49 |
Q5_K_M | 5 | 6.5 GB | High | C49 |
Q6_K | 6 | 7.4 GB | High | C49 |
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 on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server startOpciones de mejora
Hardware que ejecuta bien Yi Coder 9B
Frequently asked questions
Can NVIDIA B200 180GB run Yi Coder 9B?
Yes, NVIDIA B200 180GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 126.0 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 26.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 9B?
The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 9B run at on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Yi Coder 9B achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
Can NVIDIA B200 180GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on NVIDIA B200 180GB receives a B grade with 126.0 tok/s and 131K context.
What context window can Yi Coder 9B use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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