MacBook Pro M4 Max 96GBGünstige Wahl
96 GB Unified (+32)
C
This setup is broadly balanced for this model.21 tok/s Dekodierung
ca. $2,499 MSRP
Can Yi Coder 1.5B run on NVIDIA A16 64GB?
YES — Runs Great
D40Poor
Estimated from fit model
Yi Coder 1.5B needs ~8.7 GB VRAM. NVIDIA A16 64GB has 64.0 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) — 8.7 GB, 21.0 tok/s, Runs well
8.7 GB required64.0 GB available
14% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
5.1M
Memory
8.7 GB / 64.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsYi Coder 1.5B on NVIDIA A16 64GB
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 | D | Runs well | 21.0 tok/s | 5029 ms | 4.4M |
| Coding | D | Runs well | 21.0 tok/s | 9219 ms | 5.1M |
| Agentic Coding | D | Runs well | 21.0 tok/s | 13410 ms | 5.1M |
| Reasoning | D | Runs well | 21.0 tok/s | 10895 ms | 5.1M |
| RAG | D | Runs well | 21.0 tok/s | 16762 ms | 5.1M |
Quantization options
How Yi Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C40 |
Q3_K_S | 3 | 0.7 GB | Low | C40 |
NVFP4 | 4 | 0.8 GB | Medium | C40 |
Q4_K_M | 4 | 0.9 GB | Medium | C40 |
Q5_K_M | 5 | 1.1 GB | High | C40 |
Q6_K | 6 | 1.2 GB | High | C40 |
Q8_0 | 8 | 1.6 GB | Very High | C40 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C40 |
Get started
Copy-paste commands to run Yi Coder 1.5B on your machine.
Run
lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server startUpgrade-Optionen
Hardware, die Yi Coder 1.5B gut ausführt
MacBook Pro M3 Max 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+64)
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. $2,499 MSRP
NVIDIA DGX Spark 128GBNVIDIA-Upgrade128 GB Unified (+64)
D
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.
Frequently asked questions
Can NVIDIA A16 64GB run Yi Coder 1.5B?
Yes, NVIDIA A16 64GB can run Yi Coder 1.5B with a D grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Yi Coder 1.5B need?
Yi Coder 1.5B (1.5B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi Coder 1.5B?
The recommended quantization for Yi Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi Coder 1.5B run at on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run Yi Coder 1.5B for coding?
For coding workloads, Yi Coder 1.5B on NVIDIA A16 64GB receives a D grade with 21.0 tok/s and 5.1M context.
What context window can Yi Coder 1.5B use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, Yi Coder 1.5B can safely use up to 5.1M 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-lmstudio-community--yi-coder-1-5b-gguf-on-a16-64gb" 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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