Can Yi 9B Coder i1 run on Tesla P100 16GB?

YES — Runs Great

C54Usable

Estimated from fit model

Yi 9B Coder i1 needs ~9.3 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~79 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) — 9.3 GB, 78.7 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

78.7 tok/s

TTFT

2461 ms

Safe context

117K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on Tesla P100 16GB
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: 78.7 tok/s decode · 2.5s TTFT (warm) · 197 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	78.7 tok/s	1342 ms	117K
Coding	C	Runs well	78.7 tok/s	2461 ms	117K
Agentic Coding	C	Runs well	78.7 tok/s	3580 ms	117K
Reasoning	C	Runs well	78.7 tok/s	2908 ms	117K
RAG	C	Runs well	78.7 tok/s	4475 ms	117K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C47
Q3_K_S	3	4.4 GB	Low	C48
NVFP4	4	5.0 GB	Medium	C48
Q4_K_M	4	5.5 GB	Medium	C49
Q5_K_M	5	6.5 GB	High	C50
Q6_K	6	7.4 GB	High	C51
Q8_0Best for your GPU	8	9.6 GB	Very High	C51
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Yi 9B Coder i1 on your machine.

Run

lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server start

Frequently asked questions

Can Tesla P100 16GB run Yi 9B Coder i1?

Yes, Tesla P100 16GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 78.7 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 9B Coder i1?

The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 9B Coder i1 run at on Tesla P100 16GB?

On Tesla P100 16GB, Yi 9B Coder i1 achieves approximately 78.7 tokens per second decode speed with a time-to-first-token of 2461ms using Q4_K_M quantization.

Can Tesla P100 16GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on Tesla P100 16GB receives a C grade with 78.7 tok/s and 117K context.

What context window can Yi 9B Coder i1 use on Tesla P100 16GB?

On Tesla P100 16GB, Yi 9B Coder i1 can safely use up to 117K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Yi 9B Coder i1

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