RTX 3090 24GBGünstige Wahl24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 133%.119.3 tok/s Dekodierung
Raises estimated decode speed by about 133%.
ca. $1,499 MSRP
Can Yi Coder 9B Chat run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
Yi Coder 9B Chat needs ~9.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~51 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) — 9.7 GB, 51.1 tok/s, Runs well
9.7 GB required20.0 GB available
49% VRAM used
Fit status
Runs well
Decode
51.1 tok/s
TTFT
3785 ms
Safe context
172K
Memory
9.7 GB / 20.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat 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: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 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 | 51.1 tok/s | 2065 ms | 172K |
| Coding | C | Runs well | 51.1 tok/s | 3785 ms | 172K |
| Agentic Coding | C | Runs well | 51.1 tok/s | 5506 ms | 172K |
| Reasoning | C | Runs well | 51.1 tok/s | 4473 ms | 172K |
| RAG | C | Runs well | 51.1 tok/s | 6882 ms | 172K |
Quantization options
How Yi Coder 9B Chat (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 | C46 |
Q3_K_S | 3 | 4.4 GB | Low | C46 |
NVFP4 | 4 | 5.0 GB | Medium | C47 |
Q4_K_M | 4 | 5.5 GB | Medium | C47 |
Q5_K_M | 5 | 6.5 GB | High | C48 |
Q6_K | 6 | 7.4 GB | High | C49 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | C50 |
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 startUpgrade-Optionen
Hardware, die Yi Coder 9B Chat gut ausführt
RTX 3090 24GBGünstige WahlRTX 4090 24GBBestes Preis-Leistungs-Verhältnis24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 147%.126 tok/s Dekodierung
Raises estimated decode speed by about 147%.
ca. $1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA-Upgrade24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 101%.102.8 tok/s Dekodierung
Raises estimated decode speed by about 101%.
ca. $1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run Yi Coder 9B Chat?
Yes, RTX 4000 Ada 20GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 51.1 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 9.7 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Yi Coder 9B Chat achieves approximately 51.1 tokens per second decode speed with a time-to-first-token of 3785ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on RTX 4000 Ada 20GB receives a C grade with 51.1 tok/s and 172K context.
What context window can Yi Coder 9B Chat use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, Yi Coder 9B Chat can safely use up to 172K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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