Can Yi 9B Coder i1 run on RTX 5090 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

Yi 9B Coder i1 needs ~10.9 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~126 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 10.9 GB, 126.0 tok/s, Runs well

10.9 GB required32.0 GB available

34% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

335K

Memory

10.9 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on RTX 5090 32GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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	126.0 tok/s	838 ms	335K
Coding	C	Runs well	126.0 tok/s	1537 ms	335K
Agentic Coding	C	Runs well	126.0 tok/s	2235 ms	335K
Reasoning	C	Runs well	126.0 tok/s	1816 ms	335K
RAG	C	Runs well	126.0 tok/s	2794 ms	335K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C43
Q3_K_S	3	4.4 GB	Low	C43
NVFP4	4	5.0 GB	Medium	C43
Q4_K_M	4	5.5 GB	Medium	C43
Q5_K_M	5	6.5 GB	High	C44
Q6_K	6	7.4 GB	High	C44
Q8_0	8	9.6 GB	Very High	C45
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 5090 32GB run Yi 9B Coder i1?

Yes, RTX 5090 32GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.9 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 5090 32GB?

On RTX 5090 32GB, Yi 9B Coder i1 achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can RTX 5090 32GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 5090 32GB receives a C grade with 126.0 tok/s and 335K context.

What context window can Yi 9B Coder i1 use on RTX 5090 32GB?

On RTX 5090 32GB, Yi 9B Coder i1 can safely use up to 335K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 32GB 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

126.0 tok/s

838 ms

335K

Coding

Runs well

126.0 tok/s

1537 ms

335K

Agentic Coding

Runs well

126.0 tok/s

2235 ms

335K

Reasoning

Runs well

126.0 tok/s

1816 ms

335K

RAG

Runs well

126.0 tok/s

2794 ms

335K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C43

Q3_K_S

4.4 GB

Low

C43

NVFP4

5.0 GB

Medium

C43

Q4_K_M

5.5 GB

Medium

C43

Q5_K_M

6.5 GB

High

C44

Q6_K

7.4 GB

High

C44

Q8_0

9.6 GB

Very High

C45

F16Best for your GPU

18.5 GB

Maximum

C49

Frequently asked questions

Can RTX 5090 32GB run Yi 9B Coder i1?

Yes, RTX 5090 32GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.9 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 5090 32GB?

On RTX 5090 32GB, Yi 9B Coder i1 achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can RTX 5090 32GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 5090 32GB receives a C grade with 126.0 tok/s and 335K context.

What context window can Yi 9B Coder i1 use on RTX 5090 32GB?

On RTX 5090 32GB, Yi 9B Coder i1 can safely use up to 335K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.