RTX 3090 24GBコスパ重視24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 320%.119.3 tok/sデコード
Raises estimated decode speed by about 320%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
Can Yi Coder 9B Chat run on NVIDIA A2 16GB?
YES — Runs Great
C51Usable
Estimated from fit model
Yi Coder 9B Chat needs ~9.3 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 9.3 GB, 28.4 tok/s, Runs well
9.3 GB required16.0 GB available
58% VRAM used
Fit status
Runs well
Decode
28.4 tok/s
TTFT
6813 ms
Safe context
117K
Memory
9.3 GB / 16.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on NVIDIA A2 16GB
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: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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 | 28.4 tok/s | 3716 ms | 117K |
| Coding | C | Runs well | 28.4 tok/s | 6813 ms | 117K |
| Agentic Coding | C | Runs well | 28.4 tok/s | 9910 ms | 117K |
| Reasoning | C | Runs well | 28.4 tok/s | 8052 ms | 117K |
| RAG | C | Runs well | 28.4 tok/s | 12388 ms | 117K |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C47 |
Q3_K_S | 3 | 4.4 GB | Low | C48 |
NVFP4 | 4 | 5.0 GB | Medium | C49 |
Q4_K_M | 4 | 5.5 GB | Medium | C49 |
Q5_K_M | 5 | 6.5 GB | High | C50 |
Q6_K | 6 | 7.4 GB | High | C51 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C51 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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 startアップグレードオプション
Yi Coder 9B Chatを快適に動かすハードウェア
RTX 3090 24GBコスパ重視RTX 4090 24GBコスパ最良24 GB VRAM (+8)1008 GB/s (+808)
C
Raises estimated decode speed by about 344%.126 tok/sデコード
Raises estimated decode speed by about 344%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 262%.102.8 tok/sデコード
Raises estimated decode speed by about 262%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run Yi Coder 9B Chat?
Yes, NVIDIA A2 16GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 28.4 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 9.3 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 NVIDIA A2 16GB?
On NVIDIA A2 16GB, Yi Coder 9B Chat achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6813ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on NVIDIA A2 16GB receives a C grade with 28.4 tok/s and 117K context.
What context window can Yi Coder 9B Chat use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, Yi Coder 9B Chat can safely use up to 117K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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