Can Yi Coder 1.5B Chat run on NVIDIA L20 48GB?

YES — Runs Great

C41Usable

Estimated from fit model

Yi Coder 1.5B Chat needs ~6.8 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 6.8 GB, 24.0 tok/s, Runs well

6.8 GB required48.0 GB available

14% VRAM used

Fit status

Runs well

Decode

24.0 tok/s

TTFT

8067 ms

Safe context

3.8M

Memory

6.8 GB / 48.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B Chat on NVIDIA L20 48GB
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: 24.0 tok/s decode · 8.1s TTFT (warm) · 60 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	24.0 tok/s	4400 ms	3.3M
Coding	C	Runs well	24.0 tok/s	8067 ms	3.8M
Agentic Coding	C	Runs well	24.0 tok/s	11733 ms	3.8M
Reasoning	C	Runs well	24.0 tok/s	9533 ms	3.8M
RAG	C	Runs well	24.0 tok/s	14667 ms	3.8M

Quantization options

How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C41
Q3_K_S	3	0.7 GB	Low	C41
NVFP4	4

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 1.5B Chat well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+48)

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.

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+80)

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.

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L20 48GB run Yi Coder 1.5B Chat?

Yes, NVIDIA L20 48GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 24.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B Chat?

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

What speed will Yi Coder 1.5B Chat run at on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Yi Coder 1.5B Chat achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on NVIDIA L20 48GB receives a C grade with 24.0 tok/s and 3.8M context.

What context window can Yi Coder 1.5B Chat use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Yi Coder 1.5B Chat can safely use up to 3.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GB See all hardware for Yi Coder 1.5B Chat

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