Can Yi 9B Coder i1 run on RTX 4060 Ti 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

Yi 9B Coder i1 needs ~9.3 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~38 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.3 GB, 38.3 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

38.3 tok/s

TTFT

5055 ms

Safe context

117K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on RTX 4060 Ti 16GB
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: 38.3 tok/s decode · 5.1s TTFT (warm) · 96 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	Runs well	38.3 tok/s	2758 ms	117K
Coding	C	Runs well	38.3 tok/s	5055 ms	117K
Agentic Coding	C	Runs well	38.3 tok/s	7353 ms	117K
Reasoning	C	Runs well	38.3 tok/s	5975 ms	117K
RAG	C	Runs well	38.3 tok/s	9192 ms	117K

Inference speed

Yi 9B Coder i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 9B Coder i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	125.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	119.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	111.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	101.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	80.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	68.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	68.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	68.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	43.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	43.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	40.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	35.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C47
Q3_K_S	3	4.4 GB	Low	C48
NVFP4	4	5.0 GB	Medium	C48
Q4_K_M	4	5.5 GB	Medium	C49
Q5_K_M	5	6.5 GB	High	C50
Q6_K	6	7.4 GB	High	C51
Q8_0Best for your GPU	8	9.6 GB	Very High	C51
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

Opciones de mejora

Hardware que ejecuta bien Yi 9B Coder i1

RX 7900 XT 20GBMejor relación calidad-precio

20 GB VRAM (+4)800 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 128%.87.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 128%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RTX A4500 20GBOpción económica

20 GB VRAM (+4)640 GB/s (+352)

Sube la velocidad estimada de decodificación alrededor de un 137%.90.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 137%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,000 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run Yi 9B Coder i1?

Yes, RTX 4060 Ti 16GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 38.3 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 9.3 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, Yi 9B Coder i1 achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5055ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 4060 Ti 16GB receives a C grade with 38.3 tok/s and 117K context.

What context window can Yi 9B Coder i1 use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, Yi 9B Coder i1 can safely use up to 117K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB See all hardware for Yi 9B Coder i1

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