Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+160)
C
Adds memory headroom for longer context windows and future model growth.101.4 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $6,999 MSRP
Can Yi Coder 9B Chat run on NVIDIA H20 96GB?
YES — Runs Great
C46Usable
Estimated from fit model
Yi Coder 9B Chat needs ~17.3 GB VRAM. NVIDIA H20 96GB has 96.0 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) — 17.3 GB, 126.0 tok/s, Runs well
17.3 GB required96.0 GB available
18% VRAM used
Fit status
Runs well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
1.2M
Memory
17.3 GB / 96.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom9.6 GB
See how fast it feels
See how fast it feelsYi Coder 9B Chat on NVIDIA H20 96GB
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 | 1.2M |
| Coding | C | Runs well | 126.0 tok/s | 1537 ms | 1.2M |
| Agentic Coding | C | Runs well | 126.0 tok/s | 2235 ms | 1.2M |
| Reasoning | C | Runs well | 126.0 tok/s | 1816 ms | 1.2M |
| RAG | C | Runs well | 126.0 tok/s | 2794 ms | 1.2M |
Quantization options
How Yi Coder 9B Chat (9B params) fits at each quantization level on NVIDIA H20 96GB (96.0 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 | D39 |
Q8_0 | 8 | 9.6 GB | Very High | D39 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C40 |
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
Frequently asked questions
Can NVIDIA H20 96GB run Yi Coder 9B Chat?
Yes, NVIDIA H20 96GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 126.0 tok/s.
How much VRAM does Yi Coder 9B Chat need?
Yi Coder 9B Chat (9B parameters) requires approximately 17.3 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 H20 96GB?
On NVIDIA H20 96GB, Yi Coder 9B Chat achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
Can NVIDIA H20 96GB run Yi Coder 9B Chat for coding?
For coding workloads, Yi Coder 9B Chat on NVIDIA H20 96GB receives a C grade with 126.0 tok/s and 1.2M context.
What context window can Yi Coder 9B Chat use on NVIDIA H20 96GB?
On NVIDIA H20 96GB, Yi Coder 9B Chat can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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