Can Yi Coder 9B run on NVIDIA A16 64GB?

YES — Runs Great

B59Good

Estimated from fit model

Yi Coder 9B needs ~14.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~93 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) — 14.6 GB, 92.7 tok/s, Runs well

14.6 GB required64.0 GB available

23% VRAM used

Fit status

Runs well

Decode

92.7 tok/s

TTFT

2088 ms

Safe context

131K

Memory

14.6 GB / 64.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B on NVIDIA A16 64GB
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: 92.7 tok/s decode · 2.1s TTFT (warm) · 232 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	B	Runs well	92.7 tok/s	1139 ms	131K
Coding	B	Runs well	92.7 tok/s	2088 ms	131K
Agentic Coding	B	Runs well	92.7 tok/s	3038 ms	131K
Reasoning	B	Runs well	92.7 tok/s	2468 ms	131K
RAG	B	Runs well	92.7 tok/s	3797 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	C53
NVFP4	4	5.0 GB	Medium	C53
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C53
Q6_K	6	7.4 GB	High	C53
Q8_0	8	9.6 GB	Very High	C53
F16Best for your GPU	16	18.5 GB	Maximum	B55

Get started

Copy-paste commands to run Yi Coder 9B on your machine.

Run

lms load Yi-Coder-9B-Chat && lms server start

Upgrade options

Hardware that runs Yi Coder 9B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.74.3 tok/s decode

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

This setup is broadly balanced for this model.110.3 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Yi Coder 9B?

Yes, NVIDIA A16 64GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 92.7 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi Coder 9B achieves approximately 92.7 tokens per second decode speed with a time-to-first-token of 2088ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on NVIDIA A16 64GB receives a B grade with 92.7 tok/s and 131K context.

What context window can Yi Coder 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Yi Coder 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

92.7 tok/s

1139 ms

131K

Coding

Runs well

92.7 tok/s

2088 ms

131K

Agentic Coding

Runs well

92.7 tok/s

3038 ms

131K

Reasoning

Runs well

92.7 tok/s

2468 ms

131K

RAG

Runs well

92.7 tok/s

3797 ms

131K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C53

Q3_K_S

4.4 GB

Low

C53

NVFP4

5.0 GB

Medium

C53

Q4_K_M

5.5 GB

Medium

C53

Q5_K_M

6.5 GB

High

C53

Q6_K

7.4 GB

High

C53

Q8_0

9.6 GB

Very High

C53

F16Best for your GPU

18.5 GB

Maximum

B55

Frequently asked questions

Can NVIDIA A16 64GB run Yi Coder 9B?

Yes, NVIDIA A16 64GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 92.7 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi Coder 9B achieves approximately 92.7 tokens per second decode speed with a time-to-first-token of 2088ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on NVIDIA A16 64GB receives a B grade with 92.7 tok/s and 131K context.

What context window can Yi Coder 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.