Can Qwen 3.5 122B A10B run on NVIDIA H800 80GB?

Q: What should I upgrade first if Qwen 3.5 122B A10B feels slow on NVIDIA H800 80GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Offload

S85Excellent

Estimated from fit model

Qwen 3.5 122B A10B needs ~85.8 GB VRAM. NVIDIA H800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 85.8 GB, 74.3 tok/s, Runs with offload (needs ~5 GB host RAM)

85.8 GB required80.0 GB available

107% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~5 GB host RAM)

Decode

74.3 tok/s

TTFT

2606 ms

Safe context

Memory

85.8 GB / 80.0 GB

Offload

10%

Memory breakdown

Weights74.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3.5 122B A10B on NVIDIA H800 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: 74.3 tok/s decode · 2.6s TTFT (warm) · 186 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	69.6 tok/s	1518 ms	4K
Coding	S	Runs with offload	67.9 tok/s	2850 ms	4K
Agentic Coding	S	Very compromised	64.9 tok/s	4342 ms	4K
Reasoning	S	Runs with offload	67.9 tok/s	3368 ms	4K
RAG	S	Very compromised	64.9 tok/s	5428 ms	4K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	S90
Q3_K_SBest for your GPU	3	59.8 GB	Low	S90

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 H800 80GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	A	25.1 tok/s

Frequently asked questions

Can NVIDIA H800 80GB run Qwen 3.5 122B A10B?

Yes, NVIDIA H800 80GB can run Qwen 3.5 122B A10B with a S grade (Runs with offload). Expected decode speed: 67.9 tok/s.

How much VRAM does Qwen 3.5 122B A10B need?

Qwen 3.5 122B A10B (122B parameters) requires approximately 85.8 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 H800 80GB?

On NVIDIA H800 80GB, Qwen 3.5 122B A10B achieves approximately 67.9 tokens per second decode speed with a time-to-first-token of 2850ms using Q4_K_M quantization.

Can NVIDIA H800 80GB run Qwen 3.5 122B A10B for coding?

For coding workloads, Qwen 3.5 122B A10B on NVIDIA H800 80GB receives a S grade with 67.9 tok/s and 4K context.

What context window can Qwen 3.5 122B A10B use on NVIDIA H800 80GB?

On NVIDIA H800 80GB, Qwen 3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 122B A10B feels slow on NVIDIA H800 80GB?

See all results for NVIDIA H800 80GB See all hardware for Qwen 3.5 122B A10B

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