Can Qwen 3.5 122B A10B run on NVIDIA A800 80GB?

YES — With Offload

A84Great

○Estimated from fit model

Qwen 3.5 122B A10B needs ~85.8 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~46 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, 46.1 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

46.1 tok/s

TTFT

4196 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 A800 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: 46.1 tok/s decode · 4.2s TTFT (warm) · 115 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 5.0 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	A	Runs with offload (needs ~4 GB host RAM)	47.2 tok/s	2235 ms	4K
Coding	A	Runs with offload (needs ~5 GB host RAM)	46.1 tok/s	4196 ms	4K
Agentic Coding	A	Very compromised (needs ~6.9 GB host RAM)	44.1 tok/s	6392 ms	4K
Reasoning	A	Runs with offload (needs ~5 GB host RAM)	46.1 tok/s	4958 ms	4K
RAG	A	Very compromised (needs ~6.9 GB host RAM)	44.1 tok/s	7990 ms	4K

Quantization options

How Qwen 3.5 122B A10B (122B params) fits at each quantization level on NVIDIA A800 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
NVFP4	4	68.3 GB	Medium	F0
Q4_K_M	4	74.4 GB	Medium	F0
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 A800 80GB can run

Model	Params	Grade	Decode	Capabilities
👁 Mistral Devstral 2 123B Instruct	123B	A	15.6 tok/s

Frequently asked questions

See all results for NVIDIA A800 80GB See all hardware for Qwen 3.5 122B A10B

URL: https://willitrunai.com/can-run/qwen-3.5-122b-a10b-on-a800-80gb

⇱ Qwen 3.5 122B A10B on NVIDIA A800 80GB? YES