Can Yi 9B Coder i1 run on RTX 2080 Ti 11GB?

YES — Runs Great

B56Good

Estimated from fit model

Yi 9B Coder i1 needs ~8.8 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~73 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) — 8.8 GB, 72.9 tok/s, Runs well

8.8 GB required11.0 GB available

80% VRAM used

Fit status

Runs well

Decode

72.9 tok/s

TTFT

2655 ms

Safe context

49K

Memory

8.8 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on RTX 2080 Ti 11GB
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: 72.9 tok/s decode · 2.7s TTFT (warm) · 182 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	B	Runs well	72.9 tok/s	1448 ms	49K
Coding	B	Runs well	72.9 tok/s	2655 ms	49K
Agentic Coding	C	Tight fit	72.9 tok/s	3861 ms	49K
Reasoning	B	Runs well	72.9 tok/s	3137 ms	49K
RAG	C	Tight fit	72.9 tok/s	4826 ms	49K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C52
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
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

Upgrade options

Hardware that runs Yi 9B Coder i1 well

RTX 5070 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.77.1 tok/s decode

~$549 MSRP

RTX 3080 12GBBest value

12 GB VRAM (+1)912 GB/s (+296)

Raises estimated decode speed by about 73%.126 tok/s decode

Raises estimated decode speed by about 73%.

~$799 MSRP

RTX 3080 Ti 12GBNVIDIA upgrade

12 GB VRAM (+1)912 GB/s (+296)

Raises estimated decode speed by about 69%.122.9 tok/s decode

Raises estimated decode speed by about 69%.

~$1,199 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Yi 9B Coder i1?

Yes, RTX 2080 Ti 11GB can run Yi 9B Coder i1 with a B grade (Runs well). Expected decode speed: 72.9 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 8.8 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 RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 9B Coder i1 achieves approximately 72.9 tokens per second decode speed with a time-to-first-token of 2655ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 49K context.

What context window can Yi 9B Coder i1 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 9B Coder i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2080 Ti 11GB See all hardware for Yi 9B Coder i1

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

72.9 tok/s

1448 ms

49K

Coding

Runs well

72.9 tok/s

2655 ms

49K

Agentic Coding

Tight fit

72.9 tok/s

3861 ms

49K

Reasoning

Runs well

72.9 tok/s

3137 ms

49K

RAG

Tight fit

72.9 tok/s

4826 ms

49K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C52
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Frequently asked questions

Can RTX 2080 Ti 11GB run Yi 9B Coder i1?

Yes, RTX 2080 Ti 11GB can run Yi 9B Coder i1 with a B grade (Runs well). Expected decode speed: 72.9 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 8.8 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 RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 9B Coder i1 achieves approximately 72.9 tokens per second decode speed with a time-to-first-token of 2655ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 2080 Ti 11GB receives a B grade with 72.9 tok/s and 49K context.

What context window can Yi 9B Coder i1 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi 9B Coder i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.