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.

Capabilities:

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