Can Yi Coder 9B Chat run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

C55Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.9 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~45 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) — 8.9 GB, 44.7 tok/s, Runs well

8.9 GB required12.0 GB available

74% VRAM used

Fit status

Runs well

Decode

44.7 tok/s

TTFT

4333 ms

Safe context

62K

Memory

8.9 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RTX 3500 Ada Laptop 12GB
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: 44.7 tok/s decode · 4.3s TTFT (warm) · 112 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	44.7 tok/s	2364 ms	62K
Coding	C	Runs well	44.7 tok/s	4333 ms	62K
Agentic Coding	C	Tight fit	44.7 tok/s	6303 ms	62K
Reasoning	C	Runs well	44.7 tok/s	5121 ms	62K
RAG	C	Tight fit	44.7 tok/s	7879 ms	62K

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 RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
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

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat?

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 44.7 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.9 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat achieves approximately 44.7 tokens per second decode speed with a time-to-first-token of 4333ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RTX 3500 Ada Laptop 12GB receives a C grade with 44.7 tok/s and 62K context.

What context window can Yi Coder 9B Chat use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB 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-rtx-3500-ada-laptop-12gb" 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

44.7 tok/s

2364 ms

62K

Coding

Runs well

44.7 tok/s

4333 ms

62K

Agentic Coding

Tight fit

44.7 tok/s

6303 ms

62K

Reasoning

Runs well

44.7 tok/s

5121 ms

62K

RAG

Tight fit

44.7 tok/s

7879 ms

62K

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 RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
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 RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat?

Yes, RTX 3500 Ada Laptop 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 44.7 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.9 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat achieves approximately 44.7 tokens per second decode speed with a time-to-first-token of 4333ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RTX 3500 Ada Laptop 12GB receives a C grade with 44.7 tok/s and 62K context.

What context window can Yi Coder 9B Chat use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Yi Coder 9B Chat can safely use up to 62K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.