Can Qwen 3.6 35B A3B run on NVIDIA A100 80GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~36.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~218 tok/s.

Runtime: SGLangCapacity: RoomyBandwidth: HighStack: OptimizedBottleneck: 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) — 36.1 GB, 217.7 tok/s, Runs well

36.1 GB required80.0 GB available

45% VRAM used

Fit status

Runs well

Decode

217.7 tok/s

TTFT

889 ms

Safe context

187K

Memory

36.1 GB / 80.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime2.6 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on NVIDIA A100 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: 217.7 tok/s decode · 889ms TTFT (warm) · 544 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	Runs well	217.7 tok/s	485 ms	187K
Coding	S	Runs well	217.7 tok/s	889 ms	187K
Agentic Coding	S	Runs well	217.7 tok/s	1294 ms	187K
Reasoning	S	Runs well	217.7 tok/s	1051 ms	187K
RAG	S	Runs well	217.7 tok/s	1617 ms	187K

Inference speed

Qwen 3.6 35B A3B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.6 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 ~153 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	152.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	59.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	43.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	43.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	34.1	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	30.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	29.2	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	28.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	26.7	Tight
RTX 4080 Super 16GB	16 GB	Q4_K_M	12.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	5.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	3.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.9	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 3.6 35B A3B (35B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A83
Q3_K_S	3	17.2 GB	Low	A84
NVFP4	4	19.6 GB	Medium	A84
Q4_K_M	4	21.3 GB	Medium	A84
Q5_K_M	5	25.2 GB	High	S85
Q6_K	6	28.7 GB	High	S86
Q8_0Best for your GPU	8	37.5 GB	Very High	S88
F16	16	71.8 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "Qwen/Qwen3.6-35B-A3B" \
  --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can NVIDIA A100 80GB run Qwen 3.6 35B A3B?

Yes, NVIDIA A100 80GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 217.7 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 36.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 35B A3B?

The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 35B A3B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 35B A3B achieves approximately 217.7 tokens per second decode speed with a time-to-first-token of 889ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on NVIDIA A100 80GB receives a S grade with 217.7 tok/s and 187K context.

What context window can Qwen 3.6 35B A3B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, Qwen 3.6 35B A3B can safely use up to 187K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for Qwen 3.6 35B A3B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3.6-35b-a3b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: