Mac Studio M2 Ultra 128GB经济之选
128 GB Unified (+48)
C
This setup is broadly balanced for this model.84.5 tok/s 解码
~$3,999 MSRP
Can Yi 9B Coder i1 run on NVIDIA A100 80GB?
YES — Runs Great
C46Usable
Estimated from fit model
Yi 9B Coder i1 needs ~15.7 GB VRAM. NVIDIA A100 80GB has 80.0 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) — 15.7 GB, 126.0 tok/s, Runs well
15.7 GB required80.0 GB available
20% VRAM used
Fit status
Runs well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
991K
Memory
15.7 GB / 80.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsYi 9B Coder i1 on NVIDIA A100 80GB
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 | C | Runs well | 126.0 tok/s | 838 ms | 991K |
| Coding | C | Runs well | 126.0 tok/s | 1537 ms | 991K |
| Agentic Coding | C | Runs well | 126.0 tok/s | 2235 ms | 991K |
| Reasoning | C | Runs well | 126.0 tok/s | 1816 ms | 991K |
| RAG | C | Runs well | 126.0 tok/s | 2794 ms | 991K |
Inference speed
Yi 9B Coder i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Yi 9B Coder i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 125.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 119.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 111.3 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 101.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 80.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 68.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 43.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 43.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 40.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 35.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.4 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Yi 9B Coder i1 (9B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D39 |
Q3_K_S | 3 | 4.4 GB | Low | D39 |
NVFP4 | 4 | 5.0 GB | Medium | D39 |
Q4_K_M | 4 | 5.5 GB | Medium | D39 |
Q5_K_M | 5 | 6.5 GB | High | D39 |
Q6_K | 6 | 7.4 GB | High | D40 |
Q8_0 | 8 | 9.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C41 |
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 start升级选项
能流畅运行 Yi 9B Coder i1 的硬件
Frequently asked questions
Can NVIDIA A100 80GB run Yi 9B Coder i1?
Yes, NVIDIA A100 80GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 126.0 tok/s.
How much VRAM does Yi 9B Coder i1 need?
Yi 9B Coder i1 (9B parameters) requires approximately 15.7 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 A100 80GB?
On NVIDIA A100 80GB, Yi 9B Coder i1 achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run Yi 9B Coder i1 for coding?
For coding workloads, Yi 9B Coder i1 on NVIDIA A100 80GB receives a C grade with 126.0 tok/s and 991K context.
What context window can Yi 9B Coder i1 use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Yi 9B Coder i1 can safely use up to 991K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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