Can Yi 9B Coder i1 run on GTX 1660 Ti 6GB?

Q: What should I upgrade first if Yi 9B Coder i1 feels slow on GTX 1660 Ti 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

D39Poor

Estimated from fit model

Yi 9B Coder i1 needs ~6.4 GB VRAM. GTX 1660 Ti 6GB has 6.0 GB. With Q2_K quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

Yi 9B Coder i1 at Q4_K_M needs 8.3 GB — too much for GTX 1660 Ti 6GB (6.0 GB). Runs at Q2_K (6.4 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.3 GB, exceeds 6.0 GB available

8.3 GB required6.0 GB available

138% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.2 tok/s

TTFT

18982 ms

Safe context

Memory

8.3 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsYi 9B Coder i1 on GTX 1660 Ti 6GB
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: 10.2 tok/s decode · 19.0s TTFT (warm) · 26 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.8 tok/s	8971 ms	4K
Coding	F	Too heavy	10.2 tok/s	18982 ms	4K
Agentic Coding	F	Too heavy	7.9 tok/s	35858 ms	4K
Reasoning	F	Too heavy	10.2 tok/s	22433 ms	4K
RAG	F	Too heavy	7.9 tok/s	44822 ms	4K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on GTX 1660 Ti 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	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 3050 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.17.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 73%.

~$249 MSRP

RTX 5060 8GBNVIDIA upgrade

8 GB VRAM (+2)448 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.33.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 226%.

~$299 MSRP

RTX 3060 12GBBest value

12 GB VRAM (+6)360 GB/s (+72)

Makes the model fit on the accelerator instead of staying completely out of reach.43.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

Frequently asked questions

Can GTX 1660 Ti 6GB run Yi 9B Coder i1?

Yes, GTX 1660 Ti 6GB can run Yi 9B Coder i1 at Q2_K quantization (Runs with offload (needs ~0.2 GB host RAM)). The recommended Q4_K_M requires 8.3 GB which exceeds available memory, but at Q2_K it needs only 6.4 GB. Expected decode speed: 24.6 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 8.3 GB at Q4_K_M quantization. On GTX 1660 Ti 6GB, it fits at Q2_K using 6.4 GB.

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

The recommended quantization is Q4_K_M, but on GTX 1660 Ti 6GB the best fitting quantization is Q2_K, which uses 6.4 GB.

What speed will Yi 9B Coder i1 run at on GTX 1660 Ti 6GB?

On GTX 1660 Ti 6GB, Yi 9B Coder i1 achieves approximately 24.6 tokens per second decode speed with a time-to-first-token of 7877ms using Q2_K quantization.

Can GTX 1660 Ti 6GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on GTX 1660 Ti 6GB receives a F grade with 10.2 tok/s and 4K context.

What context window can Yi 9B Coder i1 use on GTX 1660 Ti 6GB?

On GTX 1660 Ti 6GB, Yi 9B Coder i1 can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1660 Ti 6GB See all hardware for Yi 9B Coder i1

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