Can Yi 9B Coder i1 run on RTX A5000 24GB?

YES — Runs Great

C51Usable

Estimated from fit model

Yi 9B Coder i1 needs ~10.1 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~98 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) — 10.1 GB, 97.9 tok/s, Runs well

10.1 GB required24.0 GB available

42% VRAM used

Fit status

Runs well

Decode

97.9 tok/s

TTFT

1977 ms

Safe context

226K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on RTX A5000 24GB
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: 97.9 tok/s decode · 2.0s TTFT (warm) · 245 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	97.9 tok/s	1078 ms	226K
Coding	C	Runs well	97.9 tok/s	1977 ms	226K
Agentic Coding	C	Runs well	97.9 tok/s	2876 ms	226K
Reasoning	C	Runs well	97.9 tok/s	2337 ms	226K
RAG	C	Runs well	97.9 tok/s	3595 ms	226K

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 A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C44
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C45
Q4_K_M	4	5.5 GB	Medium	C45
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C48
F16Best for your GPU	16	18.5 GB	Maximum	C49

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

Frequently asked questions

Can RTX A5000 24GB run Yi 9B Coder i1?

Yes, RTX A5000 24GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 97.9 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.1 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 A5000 24GB?

On RTX A5000 24GB, Yi 9B Coder i1 achieves approximately 97.9 tokens per second decode speed with a time-to-first-token of 1977ms using Q4_K_M quantization.

Can RTX A5000 24GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX A5000 24GB receives a C grade with 97.9 tok/s and 226K context.

What context window can Yi 9B Coder i1 use on RTX A5000 24GB?

On RTX A5000 24GB, Yi 9B Coder i1 can safely use up to 226K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A5000 24GB See all hardware for Yi 9B Coder i1

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

97.9 tok/s

1078 ms

226K

Coding

Runs well

97.9 tok/s

1977 ms

226K

Agentic Coding

Runs well

97.9 tok/s

2876 ms

226K

Reasoning

Runs well

97.9 tok/s

2337 ms

226K

RAG

Runs well

97.9 tok/s

3595 ms

226K

Yi 9B Coder i1 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 9B Coder i1 (9B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C44
Q3_K_S	3	4.4 GB	Low	C45
NVFP4	4	5.0 GB	Medium	C45
Q4_K_M	4	5.5 GB	Medium	C45
Q5_K_M	5	6.5 GB	High	C46
Q6_K	6	7.4 GB	High	C47
Q8_0	8	9.6 GB	Very High	C48
F16Best for your GPU	16	18.5 GB	Maximum	C49

Frequently asked questions

Can RTX A5000 24GB run Yi 9B Coder i1?

Yes, RTX A5000 24GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 97.9 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.1 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 A5000 24GB?

On RTX A5000 24GB, Yi 9B Coder i1 achieves approximately 97.9 tokens per second decode speed with a time-to-first-token of 1977ms using Q4_K_M quantization.

Can RTX A5000 24GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX A5000 24GB receives a C grade with 97.9 tok/s and 226K context.

What context window can Yi 9B Coder i1 use on RTX A5000 24GB?

On RTX A5000 24GB, Yi 9B Coder i1 can safely use up to 226K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.