Can Qwen 3.5 4B run on NVIDIA A800 80GB?

YES — Runs Great

A84Great

Estimated from fit model

Qwen 3.5 4B needs ~13.8 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~56 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) — 13.8 GB, 56.0 tok/s, Runs well

13.8 GB required80.0 GB available

17% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

131K

Memory

13.8 GB / 80.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.2 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3.5 4B 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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	A	Runs well	56.0 tok/s	1886 ms	131K
Coding	A	Runs well	56.0 tok/s	3457 ms	131K
Agentic Coding	A	Runs well	56.0 tok/s	5029 ms	131K
Reasoning	A	Runs well	56.0 tok/s	4086 ms	131K
RAG	A	Runs well	56.0 tok/s	6286 ms	131K

Inference speed

Qwen 3.5 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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	76.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	64.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	64.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	56.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	56.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	56.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.0	Fits

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 4B (4B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A80
Q3_K_S	3	2.0 GB	Low	A80
NVFP4	4	2.2 GB	Medium	A80
Q4_K_M	4	2.4 GB	Medium	A80
Q5_K_M	5	2.9 GB	High	A80
Q6_K	6	3.3 GB	High	A80
Q8_0	8	4.3 GB	Very High	A80
F16Best for your GPU	16	8.2 GB	Maximum	A80

Get started

Copy-paste commands to run Qwen 3.5 4B on your machine.

Run

ollama run qwen3.5:4b

Your hardware

More models your NVIDIA A800 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	15.5 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	228.2 tok/s
Qwen 3.5 27B	27B	S	99 tok/s
Qwen 3.6 27B	27B	S	99.3 tok/s
Qwen 3.5 122B A10B	122B	A	45.9 tok/s

Frequently asked questions

Can NVIDIA A800 80GB run Qwen 3.5 4B?

Yes, NVIDIA A800 80GB can run Qwen 3.5 4B with a A grade (Runs well). Expected decode speed: 56.0 tok/s.

How much VRAM does Qwen 3.5 4B need?

Qwen 3.5 4B (4B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 4B?

The recommended quantization for Qwen 3.5 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 4B run at on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen 3.5 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run Qwen 3.5 4B for coding?

For coding workloads, Qwen 3.5 4B on NVIDIA A800 80GB receives a A grade with 56.0 tok/s and 131K context.

What context window can Qwen 3.5 4B use on NVIDIA A800 80GB?

On NVIDIA A800 80GB, Qwen 3.5 4B 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 A800 80GB See all hardware for Qwen 3.5 4B

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