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

YES — Runs Great

B69Good

Estimated from fit model

Yi Coder 9B needs ~9.0 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~79 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.0 GB, 79.3 tok/s, Runs well

9.0 GB required11.0 GB available

82% VRAM used

Fit status

Runs well

Decode

79.3 tok/s

TTFT

2441 ms

Safe context

38K

Memory

9.0 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 79.3 tok/s decode · 2.4s TTFT (warm) · 198 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	79.3 tok/s	1331 ms	38K
Coding	B	Runs well	79.3 tok/s	2441 ms	38K
Agentic Coding	B	Tight fit	79.3 tok/s	3550 ms	38K
Reasoning	B	Runs well	79.3 tok/s	2885 ms	38K
RAG	B	Tight fit	79.3 tok/s	4438 ms	38K

Quantization options

How Yi Coder 9B (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	B63
Q3_K_S	3	4.4 GB	Low	B64
NVFP4	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B64
Q6_KBest for your GPU	6	7.4 GB	High	B64
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

lms load Yi-Coder-9B-Chat && lms server start

アップグレードオプション

Yi Coder 9Bを快適に動かすハードウェア

RTX 5070 12GBコスパ重視

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

This setup is broadly balanced for this model.83.9 tok/sデコード

〜$549 MSRP

RTX 3080 12GBコスパ最良

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

Raises estimated decode speed by about 36%.108 tok/sデコード

Raises estimated decode speed by about 36%.

〜$799 MSRP

RTX 3080 Ti 12GBNVIDIAアップグレード

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

Raises estimated decode speed by about 36%.108 tok/sデコード

Raises estimated decode speed by about 36%.

〜$1,199 MSRP

Frequently asked questions

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

Yes, RTX 2080 Ti 11GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 79.3 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

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

What speed will Yi Coder 9B run at on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Yi Coder 9B achieves approximately 79.3 tokens per second decode speed with a time-to-first-token of 2441ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on RTX 2080 Ti 11GB receives a B grade with 79.3 tok/s and 38K context.

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

On RTX 2080 Ti 11GB, Yi Coder 9B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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