Can Yi Coder 1.5B Chat run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

D40Poor

Estimated from fit model

Yi Coder 1.5B Chat needs ~10.3 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~21 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) — 10.3 GB, 21.0 tok/s, Runs well

10.3 GB required80.0 GB available

13% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

6.4M

Memory

10.3 GB / 80.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B Chat on NVIDIA H100 PCIe 80GB
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.

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	D	Runs well	21.0 tok/s	5029 ms	5.6M
Coding	D	Runs well	21.0 tok/s	9219 ms	6.4M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	6.4M
Reasoning	D	Runs well	21.0 tok/s	10895 ms	6.4M
RAG	C	Runs well	21.0 tok/s	16762 ms	6.4M

Quantization options

How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	D40
Q3_K_S	3	0.7 GB	Low	D40
NVFP4	4	0.8 GB	Medium	D40
Q4_K_M	4	0.9 GB	Medium	D40
Q5_K_M	5	1.1 GB	High	D40
Q6_K	6	1.2 GB	High	D40
Q8_0	8	1.6 GB	Very High	D40
F16Best for your GPU	16	3.1 GB	Maximum	D40

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

アップグレードオプション

Yi Coder 1.5B Chatを快適に動かすハードウェア

MacBook Pro M3 Max 128GBコスパ重視

128 GB Unified (+48)

This setup is broadly balanced for this model.21 tok/sデコード

〜$2,499 MSRP

Mac Studio M2 Ultra 128GBコスパ最良

128 GB Unified (+48)

This setup is broadly balanced for this model.21 tok/sデコード

〜$3,999 MSRP

NVIDIA DGX Spark 128GBNVIDIAアップグレード

128 GB Unified (+48)

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

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

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Yi Coder 1.5B Chat?

Yes, NVIDIA H100 PCIe 80GB can run Yi Coder 1.5B Chat with a D grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 10.3 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on NVIDIA H100 PCIe 80GB receives a D grade with 21.0 tok/s and 6.4M context.

What context window can Yi Coder 1.5B Chat use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Yi Coder 1.5B Chat can safely use up to 6.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 PCIe 80GB See all hardware for Yi Coder 1.5B Chat

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