Can Qwen 3.5 35B A3B run on RTX 5080 Laptop 16GB?

YES — With Q2_K

A85Great

Estimated from fit model

Qwen 3.5 35B A3B needs ~17.9 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~70 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: 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.

Qwen 3.5 35B A3B at Q4_K_M needs 25.6 GB — too much for RTX 5080 Laptop 16GB (16.0 GB). Runs at Q2_K (17.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.6 GB, exceeds 16.0 GB available

25.6 GB required16.0 GB available

160% VRAM needed

9.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

24.8 tok/s

TTFT

7796 ms

Safe context

Memory

25.6 GB / 16.0 GB

Offload

40%

Memory breakdown

Weights21.3 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3.5 35B A3B on RTX 5080 Laptop 16GB
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: 24.8 tok/s decode · 7.8s TTFT (warm) · 62 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 1.5 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	F	Too heavy	26.4 tok/s	4001 ms	4K
Coding	F	Too heavy	24.8 tok/s	7796 ms	4K
Agentic Coding	F	Too heavy	22.1 tok/s	12749 ms	4K
Reasoning	F	Too heavy	24.8 tok/s	9214 ms	4K
RAG	F	Too heavy	22.1 tok/s	15936 ms	4K

Inference speed

Qwen 3.5 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~139 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?
RTX 5090 32GB	32 GB	Q4_K_M	139.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	100.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	83.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	79.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	71.1	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	61.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	61.8	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	60.0	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	56.9	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.5	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	41.5	Tight
RTX 4080 Super 16GB	16 GB	Q4_K_M	25.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	8.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.4	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 35B A3B (35B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	F0
Q3_K_S	3	17.2 GB	Low	F0
NVFP4	4	19.6 GB	Medium	F0
Q4_K_M	4	21.3 GB	Medium	F0
Q5_K_M	5	25.2 GB	High	F0
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

Get started

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

Run

ollama run qwen3.5:35b-a3b

Upgrade-Optionen

Hardware, die Qwen 3.5 35B A3B gut ausführt

RTX 3090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+8)936 GB/s (+168)

Makes the model fit on the accelerator instead of staying completely out of reach.55.5 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 124%.

ca. $1,499 MSRP

RTX 4090 24GBNVIDIA-Upgrade

24 GB VRAM (+8)1008 GB/s (+240)

Makes the model fit on the accelerator instead of staying completely out of reach.69.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 180%.

ca. $1,599 MSRP

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+16)1792 GB/s (+1024)

Makes the model fit on the accelerator instead of staying completely out of reach.139.4 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,999 MSRP

Frequently asked questions

Can RTX 5080 Laptop 16GB run Qwen 3.5 35B A3B?

Yes, RTX 5080 Laptop 16GB can run Qwen 3.5 35B A3B at Q2_K quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 25.6 GB which exceeds available memory, but at Q2_K it needs only 17.9 GB. Expected decode speed: 70.1 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 25.6 GB at Q4_K_M quantization. On RTX 5080 Laptop 16GB, it fits at Q2_K using 17.9 GB.

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

The recommended quantization is Q4_K_M, but on RTX 5080 Laptop 16GB the best fitting quantization is Q2_K, which uses 17.9 GB.

What speed will Qwen 3.5 35B A3B run at on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Qwen 3.5 35B A3B achieves approximately 70.1 tokens per second decode speed with a time-to-first-token of 2763ms using Q2_K quantization.

Can RTX 5080 Laptop 16GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on RTX 5080 Laptop 16GB receives a F grade with 24.8 tok/s and 4K context.

What context window can Qwen 3.5 35B A3B use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Qwen 3.5 35B A3B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 35B A3B feels slow on RTX 5080 Laptop 16GB?

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.

See all results for RTX 5080 Laptop 16GB See all hardware for Qwen 3.5 35B A3B

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