RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 255%.126 tok/s 解码
Raises estimated decode speed by about 255%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Yi Coder 9B Chat run on NVIDIA L4 24GB?
YES — Runs Great
C48Usable
Estimated from fit model
Yi Coder 9B Chat needs ~10.1 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~36 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) — 10.1 GB, 35.5 tok/s, Runs well
10.1 GB required24.0 GB available
42% VRAM used
Fit status
Runs well
Decode
35.5 tok/s
TTFT
5451 ms
Safe context
226K
Memory
10.1 GB / 24.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on NVIDIA L4 24GB
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: 35.5 tok/s decode · 5.5s TTFT (warm) · 89 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 | 35.5 tok/s | 2973 ms | 226K |
| Coding | C | Runs well | 35.5 tok/s | 5451 ms | 226K |
| Agentic Coding | C | Runs well | 35.5 tok/s | 7928 ms | 226K |
| Reasoning | C | Runs well | 35.5 tok/s | 6442 ms | 226K |
| RAG | C | Runs well | 35.5 tok/s | 9910 ms | 226K |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C45 |
Q3_K_S | 3 | 4.4 GB | Low | C45 |
NVFP4 | 4 | 5.0 GB | Medium | C46 |
Q4_K_M | 4 | 5.5 GB | Medium | C46 |
Q5_K_M | 5 | 6.5 GB | High | C46 |
Q6_K | 6 | 7.4 GB | High | C47 |
Q8_0 | 8 | 9.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C50 |
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升级选项
能流畅运行 Yi Coder 9B Chat 的硬件
RTX 5090 32GB经济之选RTX PRO 4500 Blackwell 32GB最佳性价比32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 255%.126 tok/s 解码
Raises estimated decode speed by about 255%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 136%.83.9 tok/s 解码
Raises estimated decode speed by about 136%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run Yi Coder 9B Chat?
Yes, NVIDIA L4 24GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 35.5 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 10.1 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 L4 24GB?
On NVIDIA L4 24GB, Yi Coder 9B Chat achieves approximately 35.5 tokens per second decode speed with a time-to-first-token of 5451ms using Q4_K_M quantization.
Can NVIDIA L4 24GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on NVIDIA L4 24GB receives a C grade with 35.5 tok/s and 226K context.
What context window can Yi Coder 9B Chat use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, Yi Coder 9B Chat can safely use up to 226K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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