Can Yi Coder 9B Chat run on GTX 1080 Ti 11GB?

YES — Runs Great

B55Good

Estimated from fit model

Yi Coder 9B Chat needs ~8.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 8.8 GB, 52.0 tok/s, Runs well

8.8 GB required11.0 GB available

80% VRAM used

Fit status

Runs well

Decode

52.0 tok/s

TTFT

3722 ms

Safe context

49K

Memory

8.8 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on GTX 1080 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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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	52.0 tok/s	2030 ms	49K
Coding	B	Runs well	52.0 tok/s	3722 ms	49K
Agentic Coding	C	Tight fit	52.0 tok/s	5414 ms	49K
Reasoning	B	Runs well	52.0 tok/s	4399 ms	49K
RAG	C	Tight fit	52.0 tok/s	6767 ms	49K

Inference speed

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

Estimated decode speed (tokens/sec) for Yi Coder 9B Chat 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 Coder 9B Chat (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4	5.0 GB	Medium	C53
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
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 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 5070 12GBBudget pick

12 GB VRAM (+1)672 GB/s (+188)

Raises estimated decode speed by about 48%.77.1 tok/s decode

Raises estimated decode speed by about 48%.

~$549 MSRP

RTX 4070 Super 12GBBest value

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 36%.70.7 tok/s decode

Raises estimated decode speed by about 36%.

~$599 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+1)504 GB/s (+20)

Raises estimated decode speed by about 33%.68.9 tok/s decode

Raises estimated decode speed by about 33%.

~$599 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Yi Coder 9B Chat?

Yes, GTX 1080 Ti 11GB can run Yi Coder 9B Chat with a B grade (Runs well). Expected decode speed: 52.0 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.8 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Yi Coder 9B Chat achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on GTX 1080 Ti 11GB receives a B grade with 52.0 tok/s and 49K context.

What context window can Yi Coder 9B Chat use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Yi Coder 9B Chat can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for Yi Coder 9B Chat

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-gtx-1080-ti-11gb" 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

Runs well

52.0 tok/s

2030 ms

49K

Coding

Runs well

52.0 tok/s

3722 ms

49K

Agentic Coding

Tight fit

52.0 tok/s

5414 ms

49K

Reasoning

Runs well

52.0 tok/s

4399 ms

49K

RAG

Tight fit

52.0 tok/s

6767 ms

49K

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

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

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4	5.0 GB	Medium	C53
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Frequently asked questions

Can GTX 1080 Ti 11GB run Yi Coder 9B Chat?

Yes, GTX 1080 Ti 11GB can run Yi Coder 9B Chat with a B grade (Runs well). Expected decode speed: 52.0 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.8 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Yi Coder 9B Chat achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on GTX 1080 Ti 11GB receives a B grade with 52.0 tok/s and 49K context.

What context window can Yi Coder 9B Chat use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Yi Coder 9B Chat can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.