Can Qwen 3.5 122B A10B run on NVIDIA H20 96GB?

YES — Tight Fit

S96Excellent

Estimated from fit model

Qwen 3.5 122B A10B needs ~87.4 GB VRAM. NVIDIA H20 96GB has 96.0 GB. With Q4_K_M quantization, expect ~130 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 87.4 GB, 130.3 tok/s, Tight fit

87.4 GB required96.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

130.3 tok/s

TTFT

1486 ms

Safe context

73K

Memory

87.4 GB / 96.0 GB

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B 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: 130.3 tok/s decode · 1.5s TTFT (warm) · 326 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	Tight fit	130.3 tok/s	811 ms	73K
Coding	S	Tight fit	130.3 tok/s	1486 ms	73K
Agentic Coding	S	Tight fit	130.3 tok/s	2162 ms	73K
Reasoning	S	Tight fit	130.3 tok/s	1757 ms	73K
RAG	S	Tight fit	130.3 tok/s	2702 ms	73K

Inference speed

Qwen 3.5 122B A10B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 122B A10B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~35 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	34.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	28.9	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	27.4	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	21.4	Offloads
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	11.3	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.6	Too big
RTX 5090 32GB	32 GB	Q4_K_M	7.2	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	6.5	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	6.4	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.9	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	5.7	Too big
RTX 4090 24GB	24 GB	Q4_K_M	4.6	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	4.2	Too big
RTX 3090 24GB	24 GB	Q4_K_M	4.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	3.7	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.3	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 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S90
Q3_K_S	3	59.8 GB	Low	S90
NVFP4	4	68.3 GB	Medium	S90
Q4_K_MBest for your GPU	4	74.4 GB	Medium	S90
Q5_K_M	5	87.8 GB	High	F0
Q6_K	6	100.0 GB	High	F0
Q8_0	8	130.5 GB	Very High	F0
F16	16	250.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 122B A10B on your machine.

Run

lms load Qwen3.5-122B-A10B-Instruct && lms server start

Your hardware

More models your NVIDIA H20 96GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	47 tok/s

Frequently asked questions

Can NVIDIA H20 96GB run Qwen 3.5 122B A10B?

Yes, NVIDIA H20 96GB can run Qwen 3.5 122B A10B with a S grade (Tight fit). Expected decode speed: 130.3 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 87.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 122B A10B run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen 3.5 122B A10B achieves approximately 130.3 tokens per second decode speed with a time-to-first-token of 1486ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on NVIDIA H20 96GB receives a S grade with 130.3 tok/s and 73K context.

What context window can Qwen 3.5 122B A10B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen 3.5 122B A10B can safely use up to 73K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA H20 96GB See all hardware for Qwen 3.5 122B A10B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.5-122b-a10b-on-h20-96gb" 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

Tight fit

130.3 tok/s

811 ms

73K

Coding

Tight fit

130.3 tok/s

1486 ms

73K

Agentic Coding

Tight fit

130.3 tok/s

2162 ms

73K

Reasoning

Tight fit

130.3 tok/s

1757 ms

73K

RAG

Tight fit

130.3 tok/s

2702 ms

73K

Qwen 3.5 122B A10B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

34.7

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

28.9

Offloads

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

27.4

Offloads

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

21.4

Offloads

2× RX 7900 XTX 24GB

48 GB

Q4_K_M

11.3

Too big

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

10.0

Too big

2× RTX 4090 24GB

48 GB

Q4_K_M

7.6

Too big

RTX 5090 32GB

32 GB

Q4_K_M

7.2

Too big

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

7.0

Too big

2× RTX 3090 24GB

48 GB

Q4_K_M

6.5

Too big

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

6.4

Too big

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

5.9

Too big

4× RTX 3060 12GB

48 GB

Q4_K_M

5.7

Too big

RTX 4090 24GB

24 GB

Q4_K_M

4.6

Too big

RX 7900 XTX 24GB

24 GB

Q4_K_M

4.2

Too big

RTX 3090 24GB

24 GB

Q4_K_M

4.0

Too big

RTX 4080 Super 16GB

16 GB

Q4_K_M

3.7

Too big

RTX 4070 12GB

12 GB

Q4_K_M

2.3

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 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA H20 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S90
Q3_K_S	3	59.8 GB	Low	S90
NVFP4	4	68.3 GB	Medium	S90
Q4_K_MBest for your GPU	4	74.4 GB	Medium	S90
Q5_K_M	5	87.8 GB	High	F0
Q6_K	6	100.0 GB	High	F0
Q8_0	8	130.5 GB	Very High	F0
F16	16	250.1 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

47 tok/s

Frequently asked questions

Can NVIDIA H20 96GB run Qwen 3.5 122B A10B?

Yes, NVIDIA H20 96GB can run Qwen 3.5 122B A10B with a S grade (Tight fit). Expected decode speed: 130.3 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 87.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 122B A10B?

The recommended quantization for Qwen 3.5 122B A10B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 122B A10B run at on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen 3.5 122B A10B achieves approximately 130.3 tokens per second decode speed with a time-to-first-token of 1486ms using Q4_K_M quantization.

Can NVIDIA H20 96GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on NVIDIA H20 96GB receives a S grade with 130.3 tok/s and 73K context.

What context window can Qwen 3.5 122B A10B use on NVIDIA H20 96GB?

On NVIDIA H20 96GB, Qwen 3.5 122B A10B can safely use up to 73K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.