MacBook Pro M4 Max 96GBOpción económica
96 GB Unified (+32)
C
Este setup está bastante equilibrado para este modelo.68.3 tok/s decodificación
~$2,499 MSRP
Can Yi 9B Coder i1 run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
Estimated from fit model
Yi 9B Coder i1 needs ~14.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~85 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 14.1 GB, 85.2 tok/s, Runs well
14.1 GB required64.0 GB available
22% VRAM used
Fit status
Runs well
Decode
85.2 tok/s
TTFT
2271 ms
Safe context
772K
Memory
14.1 GB / 64.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 on NVIDIA A16 64GB
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: 85.2 tok/s decode · 2.3s TTFT (warm) · 213 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 | 85.2 tok/s | 1239 ms | 772K |
| Coding | C | Runs well | 85.2 tok/s | 2271 ms | 772K |
| Agentic Coding | C | Runs well | 85.2 tok/s | 3303 ms | 772K |
| Reasoning | C | Runs well | 85.2 tok/s | 2684 ms | 772K |
| RAG | C | Runs well | 85.2 tok/s | 4129 ms | 772K |
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C40 |
Q3_K_S | 3 | 4.4 GB | Low | C40 |
NVFP4 | 4 | 5.0 GB | Medium | C40 |
Q4_K_M | 4 | 5.5 GB | Medium | C40 |
Q5_K_M | 5 | 6.5 GB | High | C40 |
Q6_K | 6 | 7.4 GB | High | C40 |
Q8_0 | 8 | 9.6 GB | Very High | C41 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C43 |
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 startOpciones de mejora
Hardware que ejecuta bien Yi 9B Coder i1
Frequently asked questions
Can NVIDIA A16 64GB run Yi 9B Coder i1?
Yes, NVIDIA A16 64GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 85.2 tok/s.
How much VRAM does Yi 9B Coder i1 need?
Yi 9B Coder i1 (9B parameters) requires approximately 14.1 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi 9B Coder i1 achieves approximately 85.2 tokens per second decode speed with a time-to-first-token of 2271ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on NVIDIA A16 64GB receives a C grade with 85.2 tok/s and 772K context.
What context window can Yi 9B Coder i1 use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi 9B Coder i1 can safely use up to 772K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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