Can Qwen 2.5 32B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

A84Great

Estimated from fit model

Qwen 2.5 32B needs ~32.6 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~93 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) — 32.6 GB, 93.0 tok/s, Runs well

32.6 GB required80.0 GB available

41% VRAM used

Fit status

Runs well

Decode

93.0 tok/s

TTFT

2083 ms

Safe context

131K

Memory

32.6 GB / 80.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 32B 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: 93.0 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	A	Runs well	93.0 tok/s	1136 ms	131K
Coding	A	Runs well	93.0 tok/s	2083 ms	131K
Agentic Coding	S	Runs well	93.0 tok/s	3030 ms	131K
Reasoning	A	Runs well	93.0 tok/s	2462 ms	131K
RAG	S	Runs well	93.0 tok/s	3787 ms	131K

Inference speed

Qwen 2.5 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	30.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.8	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.9	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	20.9	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	13.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.1	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A74
Q3_K_S	3	15.7 GB	Low	A75
NVFP4	4	17.9 GB	Medium	A75
Q4_K_M	4	19.5 GB	Medium	A75
Q5_K_M	5	23.0 GB	High	A76
Q6_K	6	26.2 GB	High	A77
Q8_0	8	34.2 GB	Very High	A78
F16Best for your GPU	16	65.6 GB	Maximum	A81

Get started

Copy-paste commands to run Qwen 2.5 32B on your machine.

Run

ollama run qwen2.5

Your hardware

More models your NVIDIA H100 PCIe 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	14.8 tok/s
Qwen 3.5 122B A10B	122B	A	44.5 tok/s
Qwen 3.6 35B A3B	35B	S	213.5 tok/s
Qwen 3.5 35B A3B	35B	S	232.2 tok/s
Mistral Small 4 119B	119B	A	47 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen 2.5 32B?

Yes, NVIDIA H100 PCIe 80GB can run Qwen 2.5 32B with a A grade (Runs well). Expected decode speed: 93.0 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 2.5 32B achieves approximately 93.0 tokens per second decode speed with a time-to-first-token of 2083ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on NVIDIA H100 PCIe 80GB receives a A grade with 93.0 tok/s and 131K context.

What context window can Qwen 2.5 32B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 2.5 32B 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 2.5 32B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-32b-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

93.0 tok/s

1136 ms

131K

Coding

Runs well

93.0 tok/s

2083 ms

131K

Agentic Coding

Runs well

93.0 tok/s

3030 ms

131K

Reasoning

Runs well

93.0 tok/s

2462 ms

131K

RAG

Runs well

93.0 tok/s

3787 ms

131K

Qwen 2.5 32B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

66.4

Tight

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

33.2

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

33.2

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

30.8

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

25.7

Fits

RTX 4090 24GB

24 GB

Q4_K_M

24.8

Heavy offload

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

24.3

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

22.9

Heavy offload

RTX 3090 24GB

24 GB

Q4_K_M

21.2

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

20.9

Tight

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

13.3

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

12.2

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

9.0

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.1

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.0

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Qwen 2.5 32B (32B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A74
Q3_K_S	3	15.7 GB	Low	A75
NVFP4	4	17.9 GB	Medium	A75
Q4_K_M	4	19.5 GB	Medium	A75
Q5_K_M	5	23.0 GB	High	A76
Q6_K	6	26.2 GB	High	A77
Q8_0	8	34.2 GB	Very High	A78
F16Best for your GPU	16	65.6 GB	Maximum	A81

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

14.8 tok/s

Qwen 3.5 122B A10B

122B

44.5 tok/s

Qwen 3.6 35B A3B

35B

213.5 tok/s

Qwen 3.5 35B A3B

35B

232.2 tok/s

Mistral Small 4 119B

119B

47 tok/s

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run Qwen 2.5 32B?

Yes, NVIDIA H100 PCIe 80GB can run Qwen 2.5 32B with a A grade (Runs well). Expected decode speed: 93.0 tok/s.

How much VRAM does Qwen 2.5 32B need?

Qwen 2.5 32B (32B parameters) requires approximately 32.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 32B?

The recommended quantization for Qwen 2.5 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 32B run at on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 2.5 32B achieves approximately 93.0 tokens per second decode speed with a time-to-first-token of 2083ms using Q4_K_M quantization.

Can NVIDIA H100 PCIe 80GB run Qwen 2.5 32B for coding?

For coding workloads, Qwen 2.5 32B on NVIDIA H100 PCIe 80GB receives a A grade with 93.0 tok/s and 131K context.

What context window can Qwen 2.5 32B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, Qwen 2.5 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.