Can Qwen 3.5 35B A3B run on NVIDIA H200 141GB?

YES — Runs Great

S89Excellent

Estimated from fit model

Qwen 3.5 35B A3B needs ~38.1 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~557 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) — 38.1 GB, 557.2 tok/s, Runs well

38.1 GB required141.0 GB available

27% VRAM used

Fit status

Runs well

Decode

557.2 tok/s

TTFT

350 ms

Safe context

131K

Memory

38.1 GB / 141.0 GB

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B on NVIDIA H200 141GB
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: 557.2 tok/s decode · 350ms TTFT (warm) · 1393 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	557.2 tok/s	350 ms	131K
Coding	S	Runs well	557.2 tok/s	350 ms	131K
Agentic Coding	S	Runs well	557.2 tok/s	505 ms	131K
Reasoning	S	Runs well	557.2 tok/s	411 ms	131K
RAG	S	Runs well	557.2 tok/s	632 ms	131K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A80
Q3_K_S	3	17.2 GB	Low	A80
NVFP4	4	19.6 GB	Medium	A80
Q4_K_M	4	21.3 GB	Medium	A80
Q5_K_M	5	25.2 GB	High	A81
Q6_K	6	28.7 GB	High	A81
Q8_0	8	37.5 GB	Very High	A83
F16Best for your GPU	16	71.8 GB	Maximum	S88

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Your hardware

More models your NVIDIA H200 141GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	58.4 tok/s
Qwen 3.5 122B A10B	122B	S	162.1 tok/s

Frequently asked questions

Can NVIDIA H200 141GB run Qwen 3.5 35B A3B?

Yes, NVIDIA H200 141GB can run Qwen 3.5 35B A3B with a S grade (Runs well). Expected decode speed: 557.2 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 38.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Qwen 3.5 35B A3B achieves approximately 557.2 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on NVIDIA H200 141GB receives a S grade with 557.2 tok/s and 131K context.

What context window can Qwen 3.5 35B A3B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Qwen 3.5 35B A3B 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 H200 141GB See all hardware for Qwen 3.5 35B A3B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.5-35b-a3b-on-h200-141gb" 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

557.2 tok/s

350 ms

131K

Coding

Runs well

557.2 tok/s

350 ms

131K

Agentic Coding

Runs well

557.2 tok/s

505 ms

131K

Reasoning

Runs well

557.2 tok/s

411 ms

131K

RAG

Runs well

557.2 tok/s

632 ms

131K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A80
Q3_K_S	3	17.2 GB	Low	A80
NVFP4	4	19.6 GB	Medium	A80
Q4_K_M	4	21.3 GB	Medium	A80
Q5_K_M	5	25.2 GB	High	A81
Q6_K	6	28.7 GB	High	A81
Q8_0	8	37.5 GB	Very High	A83
F16Best for your GPU	16	71.8 GB	Maximum	S88

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

58.4 tok/s

Qwen 3.5 122B A10B

122B

162.1 tok/s

Frequently asked questions

Can NVIDIA H200 141GB run Qwen 3.5 35B A3B?

Yes, NVIDIA H200 141GB can run Qwen 3.5 35B A3B with a S grade (Runs well). Expected decode speed: 557.2 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 38.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Qwen 3.5 35B A3B achieves approximately 557.2 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on NVIDIA H200 141GB receives a S grade with 557.2 tok/s and 131K context.

What context window can Qwen 3.5 35B A3B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Qwen 3.5 35B A3B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.