Can Qwen 3.5 27B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen 3.5 27B needs ~28.8 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~110 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) — 28.8 GB, 110.2 tok/s, Runs well

28.8 GB required80.0 GB available

36% VRAM used

Fit status

Runs well

Decode

110.2 tok/s

TTFT

1757 ms

Safe context

131K

Memory

28.8 GB / 80.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3.5 27B 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: 110.2 tok/s decode · 1.8s TTFT (warm) · 275 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	S	Runs well	110.2 tok/s	959 ms	131K
Coding	S	Runs well	110.2 tok/s	1757 ms	131K
Agentic Coding	S	Runs well	110.2 tok/s	2556 ms	131K
Reasoning	S	Runs well	110.2 tok/s	2077 ms	131K
RAG	S	Runs well	110.2 tok/s	3195 ms	131K

Quantization options

How Qwen 3.5 27B (27B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A83
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4	15.1 GB	Medium	A84
Q4_K_M	4	16.5 GB	Medium	A84
Q5_K_M	5	19.4 GB	High	A84
Q6_K	6	22.1 GB	High	A85
Q8_0	8	28.9 GB	Very High	S86
F16Best for your GPU	16	55.4 GB	Maximum	S90

Get started

Copy-paste commands to run Qwen 3.5 27B on your machine.

Run

ollama run qwen3.5:27b

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	254 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen 3.5 27B?

Yes, NVIDIA H100 PCIe 80GB can run Qwen 3.5 27B with a S grade (Runs well). Expected decode speed: 110.2 tok/s.

How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B (27B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 27B?

The recommended quantization for Qwen 3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 27B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 3.5 27B achieves approximately 110.2 tokens per second decode speed with a time-to-first-token of 1757ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen 3.5 27B for coding?

For coding workloads, Qwen 3.5 27B on NVIDIA H100 PCIe 80GB receives a S grade with 110.2 tok/s and 131K context.

What context window can Qwen 3.5 27B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 3.5 27B 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 H100 PCIe 80GB See all hardware for Qwen 3.5 27B

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<iframe src="https://willitrunai.com/embed/qwen-3.5-27b-on-h100-pcie-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

110.2 tok/s

959 ms

131K

Coding

Runs well

110.2 tok/s

1757 ms

131K

Agentic Coding

Runs well

110.2 tok/s

2556 ms

131K

Reasoning

Runs well

110.2 tok/s

2077 ms

131K

RAG

Runs well

110.2 tok/s

3195 ms

131K

Quantization options

How Qwen 3.5 27B (27B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A83
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4	15.1 GB	Medium	A84
Q4_K_M	4	16.5 GB	Medium	A84
Q5_K_M	5	19.4 GB	High	A84
Q6_K	6	22.1 GB	High	A85
Q8_0	8	28.9 GB	Very High	S86
F16Best for your GPU	16	55.4 GB	Maximum	S90

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

14.8 tok/s

Qwen3-Coder 30B A3B Instruct

30.5B

254 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen 3.5 27B?

Yes, NVIDIA H100 PCIe 80GB can run Qwen 3.5 27B with a S grade (Runs well). Expected decode speed: 110.2 tok/s.

How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B (27B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 27B?

The recommended quantization for Qwen 3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 27B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 3.5 27B achieves approximately 110.2 tokens per second decode speed with a time-to-first-token of 1757ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen 3.5 27B for coding?

For coding workloads, Qwen 3.5 27B on NVIDIA H100 PCIe 80GB receives a S grade with 110.2 tok/s and 131K context.

What context window can Qwen 3.5 27B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 3.5 27B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.