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

YES — Tight Fit

C54Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~105 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) — 8.7 GB, 105.2 tok/s, Tight fit

8.7 GB required10.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

105.2 tok/s

TTFT

1840 ms

Safe context

35K

Memory

8.7 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat 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: 105.2 tok/s decode · 1.8s TTFT (warm) · 263 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	C	Tight fit	105.2 tok/s	1004 ms	35K
Coding	C	Tight fit	105.2 tok/s	1840 ms	35K
Agentic Coding	C	Runs with offload	105.2 tok/s	2677 ms	35K
Reasoning	C	Tight fit	105.2 tok/s	2175 ms	35K
RAG	C	Runs with offload	105.2 tok/s	3346 ms	35K

Quantization options

How Yi Coder 9B Chat (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	C52
Q3_K_S	3	4.4 GB	Low	C53
NVFP4	4

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 9B Chat well

RTX 3060 12GBBudget pick

12 GB VRAM (+2)

This setup is broadly balanced for this model.43.3 tok/s decode

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+2)

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

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+2)

This setup is broadly balanced for this model.70.7 tok/s decode

~$599 MSRP

Frequently asked questions

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

Yes, RTX 3080 10GB can run Yi Coder 9B Chat with a C grade (Tight fit). Expected decode speed: 105.2 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

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

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

What speed will Yi Coder 9B Chat run at on RTX 3080 10GB?

On RTX 3080 10GB, Yi Coder 9B Chat achieves approximately 105.2 tokens per second decode speed with a time-to-first-token of 1840ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B Chat on RTX 3080 10GB receives a C grade with 105.2 tok/s and 35K context.

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

On RTX 3080 10GB, Yi Coder 9B Chat can safely use up to 35K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-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: