RTX 3090 24GB经济之选24 GB VRAM (+4)936 GB/s (+576)
B
Raises estimated decode speed by about 127%.126 tok/s 解码
Raises estimated decode speed by about 127%.
~$1,499 MSRP
Can Yi Coder 9B run on RTX 4000 Ada 20GB?
YES — Runs Great
B63Good
Estimated from fit model
Yi Coder 9B needs ~10.2 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.2 GB, 55.6 tok/s, Runs well
10.2 GB required20.0 GB available
51% VRAM used
Fit status
Runs well
Decode
55.6 tok/s
TTFT
3481 ms
Safe context
124K
Memory
10.2 GB / 20.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsYi Coder 9B on RTX 4000 Ada 20GB
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: 55.6 tok/s decode · 3.5s TTFT (warm) · 139 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 | 55.6 tok/s | 1898 ms | 124K |
| Coding | B | Runs well | 55.6 tok/s | 3481 ms | 124K |
| Agentic Coding | B | Runs well | 55.6 tok/s | 5063 ms | 124K |
| Reasoning | B | Runs well | 55.6 tok/s | 4113 ms | 124K |
| RAG | B | Runs well | 55.6 tok/s | 6328 ms | 124K |
Quantization options
How Yi Coder 9B (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B58 |
Q3_K_S | 3 | 4.4 GB | Low | B59 |
NVFP4 | 4 | 5.0 GB | Medium | B59 |
Q4_K_M | 4 | 5.5 GB | Medium | B59 |
Q5_K_M | 5 | 6.5 GB | High | B60 |
Q6_K | 6 | 7.4 GB | High | B61 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | B63 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Yi Coder 9B on your machine.
Run
lms load Yi-Coder-9B-Chat && lms server start升级选项
能流畅运行 Yi Coder 9B 的硬件
RTX 3090 24GB经济之选RTX 4090 24GB最佳性价比24 GB VRAM (+4)1008 GB/s (+648)
B
Raises estimated decode speed by about 127%.126 tok/s 解码
Raises estimated decode speed by about 127%.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA 升级24 GB VRAM (+4)672 GB/s (+312)
B
Raises estimated decode speed by about 101%.111.8 tok/s 解码
Raises estimated decode speed by about 101%.
~$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run Yi Coder 9B?
Yes, RTX 4000 Ada 20GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 55.6 tok/s.
How much VRAM does Yi Coder 9B need?
Yi Coder 9B (9B parameters) requires approximately 10.2 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Yi Coder 9B achieves approximately 55.6 tokens per second decode speed with a time-to-first-token of 3481ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run Yi Coder 9B for coding?
For coding workloads, Yi Coder 9B on RTX 4000 Ada 20GB receives a B grade with 55.6 tok/s and 124K context.
What context window can Yi Coder 9B use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Yi Coder 9B can safely use up to 124K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
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