Can Qwen 2.5 Coder 32B run on NVIDIA H20 96GB?

YES — Runs Great

A77Great

○Estimated from fit model

Qwen 2.5 Coder 32B needs ~34.2 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~179 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) — 34.2 GB, 179.3 tok/s, Runs well

34.2 GB required96.0 GB available

36% VRAM used

Fit status

Runs well

Decode

179.3 tok/s

TTFT

1080 ms

Safe context

131K

Memory

34.2 GB / 96.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B on NVIDIA H20 96GB
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: 179.3 tok/s decode · 1.1s TTFT (warm) · 448 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	179.3 tok/s	589 ms	131K
Coding	A	Runs well	179.3 tok/s	1080 ms	131K
Agentic Coding	A	Runs well	179.3 tok/s	1571 ms	131K
Reasoning	A	Runs well	179.3 tok/s	1276 ms	131K
RAG	A	Runs well	179.3 tok/s	1964 ms	131K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	B68
Q3_K_S	3	15.7 GB	Low	B68
NVFP4	4	17.9 GB	Medium	B68
Q4_K_M	4	19.5 GB	Medium	B68
Q5_K_M	5	23.0 GB	High	B69
Q6_K	6	26.2 GB	High	B69
Q8_0	8	34.2 GB	Very High	A71
F16Best for your GPU	16	65.6 GB	Maximum	A76

Get started

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

Run

ollama run qwen2.5-coder

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	47 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	130.3 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	411.7 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	447.8 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	141.2 tok/s

Frequently asked questions

See all results for NVIDIA H20 96GB See all hardware for Qwen 2.5 Coder 32B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-32b-on-h20-96gb

⇱ Qwen 2.5 Coder 32B on NVIDIA H20 96GB? YES