Can Qwen 3.6 35B A3B run on NVIDIA L20 48GB?

YES — Runs Great

S98Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~32.9 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~80 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) — 32.9 GB, 80.2 tok/s, Runs well

32.9 GB required48.0 GB available

69% VRAM used

Fit status

Runs well

Decode

80.2 tok/s

TTFT

2415 ms

Safe context

75K

Memory

32.9 GB / 48.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime2.6 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on NVIDIA L20 48GB
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: 80.2 tok/s decode · 2.4s TTFT (warm) · 200 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	80.2 tok/s	1317 ms	75K
Coding	S	Runs well	80.2 tok/s	2415 ms	75K
Agentic Coding	S	Runs well	80.2 tok/s	3513 ms	75K
Reasoning	S	Runs well	80.2 tok/s	2855 ms	75K
RAG	S	Runs well	80.2 tok/s	4392 ms	75K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	S86
Q3_K_S	3	17.2 GB	Low	S88
NVFP4	4	19.6 GB	Medium	S88
Q4_K_M	4	21.3 GB	Medium	S89
Q5_K_M	5	25.2 GB	High	S90
Q6_K	6	28.7 GB	High	S90
Q8_0Best for your GPU	8	37.5 GB	Very High	S90
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 L20 48GB run Qwen 3.6 35B A3B?

Yes, NVIDIA L20 48GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 80.2 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 32.9 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 L20 48GB?

On NVIDIA L20 48GB, Qwen 3.6 35B A3B achieves approximately 80.2 tokens per second decode speed with a time-to-first-token of 2415ms using Q4_K_M quantization.

Can NVIDIA L20 48GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on NVIDIA L20 48GB receives a S grade with 80.2 tok/s and 75K context.

What context window can Qwen 3.6 35B A3B use on NVIDIA L20 48GB?

On NVIDIA L20 48GB, Qwen 3.6 35B A3B can safely use up to 75K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

See all results for NVIDIA L20 48GB See all hardware for Qwen 3.6 35B A3B

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