Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.21 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $6,999 MSRP
Can Yi Coder 1.5B Chat run on Intel Data Center GPU Max 1550 128GB?
YES — Runs Great
D40Poor
Estimated from fit model
Yi Coder 1.5B Chat needs ~14.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 14.8 GB, 21.0 tok/s, Runs well
14.8 GB required128.0 GB available
12% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
10.3M
Memory
14.8 GB / 128.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsYi Coder 1.5B Chat on Intel Data Center GPU Max 1550 128GB
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
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 21.0 tok/s | 5029 ms | 9.1M |
| Coding | D | Runs well | 21.0 tok/s | 9219 ms | 10.3M |
| Agentic Coding | D | Runs well | 21.0 tok/s | 13410 ms | 10.3M |
| Reasoning | D | Runs well | 21.0 tok/s | 10895 ms | 10.3M |
| RAG | D | Runs well | 21.0 tok/s | 16762 ms | 10.3M |
Quantization options
How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D38 |
Q3_K_S | 3 | 0.7 GB | Low | D38 |
NVFP4 | 4 | 0.8 GB | Medium | D38 |
Q4_K_M | 4 | 0.9 GB | Medium | D38 |
Q5_K_M | 5 | 1.1 GB | High | D38 |
Q6_K | 6 | 1.2 GB | High | D38 |
Q8_0 | 8 | 1.6 GB | Very High | D38 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | D38 |
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 startUpgrade-Optionen
Hardware, die Yi Coder 1.5B Chat gut ausführt
Frequently asked questions
Can Intel Data Center GPU Max 1550 128GB run Yi Coder 1.5B Chat?
Yes, Intel Data Center GPU Max 1550 128GB can run Yi Coder 1.5B Chat with a D 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 14.8 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 Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, 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 Intel Data Center GPU Max 1550 128GB run Yi Coder 1.5B Chat for coding?
For coding workloads, Yi Coder 1.5B Chat on Intel Data Center GPU Max 1550 128GB receives a D grade with 21.0 tok/s and 10.3M context.
What context window can Yi Coder 1.5B Chat use on Intel Data Center GPU Max 1550 128GB?
On Intel Data Center GPU Max 1550 128GB, Yi Coder 1.5B Chat can safely use up to 10.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Yi Coder 1.5B Chat feels slow on Intel Data Center GPU Max 1550 128GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Yi Coder 1.5B Chat?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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