Can Yi Coder 1.5B run on NVIDIA T4 16GB?

YES — Runs Great

C42Usable

Estimated from fit model

Yi Coder 1.5B needs ~3.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~21 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) — 3.9 GB, 21.0 tok/s, Runs well

3.9 GB required16.0 GB available

24% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

1.1M

Memory

3.9 GB / 16.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B on NVIDIA T4 16GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	21.0 tok/s	5029 ms	983K
Coding	C	Runs well	21.0 tok/s	9219 ms	1.1M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	1.1M
Reasoning	C	Runs well	21.0 tok/s	10895 ms	1.1M
RAG	C	Runs well	21.0 tok/s	16762 ms	1.1M

Quantization options

How Yi Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C45
Q3_K_S	3	0.7 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run Yi Coder 1.5B on your machine.

Run

lms load hf-lmstudio-community--yi-coder-1-5b-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 1.5B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

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

~$799 MSRP

MacBook Pro M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.21 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run Yi Coder 1.5B?

Yes, NVIDIA T4 16GB can run Yi Coder 1.5B with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B need?

Yi Coder 1.5B (1.5B parameters) requires approximately 3.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B?

The recommended quantization for Yi Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Yi Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run Yi Coder 1.5B for coding?

For coding workloads, Yi Coder 1.5B on NVIDIA T4 16GB receives a C grade with 21.0 tok/s and 1.1M context.

What context window can Yi Coder 1.5B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, Yi Coder 1.5B can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for Yi Coder 1.5B

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