Mac mini M4 64GBコスパ重視
64 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.21 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Can Yi Coder 1.5B Chat run on RTX 5000 Ada 32GB?
YES — Runs Great
C41Usable
Estimated from fit model
Yi Coder 1.5B Chat needs ~5.5 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 5.5 GB, 21.0 tok/s, Runs well
5.5 GB required32.0 GB available
17% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
2.4M
Memory
5.5 GB / 32.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsYi Coder 1.5B Chat on RTX 5000 Ada 32GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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 | 21.0 tok/s | 5029 ms | 2.1M |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 2.4M |
| Agentic Coding | C | Runs well | 21.0 tok/s | 13410 ms | 2.4M |
| Reasoning | C | Runs well | 21.0 tok/s | 10895 ms | 2.4M |
| RAG | C | Runs well | 21.0 tok/s | 16762 ms | 2.4M |
Quantization options
How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C43 |
Q3_K_S | 3 | 0.7 GB | Low | C43 |
NVFP4 | 4 | 0.8 GB | Medium | C43 |
Q4_K_M | 4 | 0.9 GB | Medium | C43 |
Q5_K_M | 5 | 1.1 GB | High | C43 |
Q6_K | 6 | 1.2 GB | High | C43 |
Q8_0 | 8 | 1.6 GB | Very High | C43 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C43 |
Get started
Copy-paste commands to run Yi Coder 1.5B Chat on your machine.
Run
lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server startアップグレードオプション
Yi Coder 1.5B Chatを快適に動かすハードウェア
Frequently asked questions
Can RTX 5000 Ada 32GB run Yi Coder 1.5B Chat?
Yes, RTX 5000 Ada 32GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Yi Coder 1.5B Chat need?
Yi Coder 1.5B Chat (1.5B parameters) requires approximately 5.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 1.5B Chat?
The recommended quantization for Yi Coder 1.5B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 1.5B Chat run at on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can RTX 5000 Ada 32GB run Yi Coder 1.5B Chat for coding?
For coding workloads, Yi Coder 1.5B Chat on RTX 5000 Ada 32GB receives a C grade with 21.0 tok/s and 2.4M context.
What context window can Yi Coder 1.5B Chat use on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Yi Coder 1.5B Chat can safely use up to 2.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-1-5b-chat-gguf-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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