Can Yi Coder 9B run on RTX 3080 10GB?

YES — Tight Fit

B66Good

Estimated from fit model

Yi Coder 9B needs ~9.2 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~114 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 9.2 GB, 114.4 tok/s, Tight fit

9.2 GB required10.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

114.4 tok/s

TTFT

1692 ms

Safe context

25K

Memory

9.2 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B on RTX 3080 10GB
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: 114.4 tok/s decode · 1.7s TTFT (warm) · 286 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	B	Tight fit	114.4 tok/s	923 ms	25K
Coding	B	Tight fit	114.4 tok/s	1692 ms	25K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	75.6 tok/s	3724 ms	25K
Reasoning	B	Tight fit	114.4 tok/s	2000 ms	25K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	75.6 tok/s	4655 ms	25K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B64
Q3_K_S	3	4.4 GB	Low	B65
NVFP4	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_MBest for your GPU	5	6.5 GB	High	B64
Q6_K	6	7.4 GB	High	F0
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

Upgrade options

Hardware that runs Yi Coder 9B well

RTX 3060 12GBBudget pick

12 GB VRAM (+2)

This setup is broadly balanced for this model.47.1 tok/s decode

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+2)

This setup is broadly balanced for this model.83.9 tok/s decode

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+2)

This setup is broadly balanced for this model.76.9 tok/s decode

~$599 MSRP

Frequently asked questions

Can RTX 3080 10GB run Yi Coder 9B?

Yes, RTX 3080 10GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 114.4 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.2 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 3080 10GB?

On RTX 3080 10GB, Yi Coder 9B achieves approximately 114.4 tokens per second decode speed with a time-to-first-token of 1692ms using Q4_K_M quantization.

Can RTX 3080 10GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RTX 3080 10GB receives a B grade with 114.4 tok/s and 25K context.

What context window can Yi Coder 9B use on RTX 3080 10GB?

On RTX 3080 10GB, Yi Coder 9B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 3080 10GB See all hardware for Yi Coder 9B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/yi-coder-9b-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

114.4 tok/s

923 ms

25K

Coding

Tight fit

114.4 tok/s

1692 ms

25K

Agentic Coding

Runs with offload (needs ~0.3 GB host RAM)

75.6 tok/s

3724 ms

25K

Reasoning

Tight fit

114.4 tok/s

2000 ms

25K

RAG

Runs with offload (needs ~0.3 GB host RAM)

75.6 tok/s

4655 ms

25K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

B64

Q3_K_S

4.4 GB

Low

B65

NVFP4

5.0 GB

Medium

B65

Q4_K_M

5.5 GB

Medium

B65

Q5_K_MBest for your GPU

6.5 GB

High

B64

Q6_K

7.4 GB

High

Q8_0

9.6 GB

Very High

F16

18.5 GB

Maximum

Frequently asked questions

Can RTX 3080 10GB run Yi Coder 9B?

Yes, RTX 3080 10GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 114.4 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.2 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 3080 10GB?

On RTX 3080 10GB, Yi Coder 9B achieves approximately 114.4 tokens per second decode speed with a time-to-first-token of 1692ms using Q4_K_M quantization.

Can RTX 3080 10GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on RTX 3080 10GB receives a B grade with 114.4 tok/s and 25K context.

What context window can Yi Coder 9B use on RTX 3080 10GB?

On RTX 3080 10GB, Yi Coder 9B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.