Can Yi 9B Coder i1 run on RTX 2070 Super 8GB?

Q: What should I upgrade first if Yi 9B Coder i1 feels slow on RTX 2070 Super 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C50Usable

Estimated from fit model

Yi 9B Coder i1 needs ~8.2 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 8.2 GB, 34.0 tok/s, Runs with offload (needs ~0.2 GB host RAM)

8.2 GB required8.0 GB available

102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

34.0 tok/s

TTFT

5697 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on RTX 2070 Super 8GB
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: 34.0 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	49.8 tok/s	2121 ms	12K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	34.0 tok/s	5697 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	26.1 tok/s	10794 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	34.0 tok/s	6733 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	26.1 tok/s

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	C53
NVFP4Best for your GPU

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

Upgrade options

Hardware that runs Yi 9B Coder i1 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.36.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)

Raises estimated decode speed by about 49%.50.6 tok/s decode

Raises estimated decode speed by about 49%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.35.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run Yi 9B Coder i1?

Yes, RTX 2070 Super 8GB can run Yi 9B Coder i1 with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 34.0 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 8.2 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 2070 Super 8GB?

On RTX 2070 Super 8GB, Yi 9B Coder i1 achieves approximately 34.0 tokens per second decode speed with a time-to-first-token of 5697ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on RTX 2070 Super 8GB receives a C grade with 34.0 tok/s and 12K context.

What context window can Yi 9B Coder i1 use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, Yi 9B Coder i1 can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi 9B Coder i1 feels slow on RTX 2070 Super 8GB?

See all results for RTX 2070 Super 8GB See all hardware for Yi 9B Coder i1

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