Can Gemma 3 27B run on NVIDIA A16 64GB?

YES — Runs Great

A83Great

Estimated from fit model

Gemma 3 27B needs ~35.3 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 35.3 GB, 29.8 tok/s, Runs well

35.3 GB required64.0 GB available

55% VRAM used

Fit status

Runs well

Decode

29.8 tok/s

TTFT

6489 ms

Safe context

57K

Memory

35.3 GB / 64.0 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsGemma 3 27B on NVIDIA A16 64GB
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: 29.8 tok/s decode · 6.5s TTFT (warm) · 75 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	29.8 tok/s	3539 ms	57K
Coding	A	Runs well	29.8 tok/s	6489 ms	57K
Agentic Coding	S	Runs well	29.8 tok/s	9438 ms	57K
Reasoning	A	Runs well	29.8 tok/s	7669 ms	57K
RAG	S	Runs well	29.8 tok/s	11798 ms	57K

Inference speed

Gemma 3 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~58 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	58.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	26.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	26.6	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	26.6	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	22.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.3	Fits
RTX 4090 24GB	24 GB	Q4_K_M	20.9	Too big
RTX 3090 24GB	24 GB	Q4_K_M	17.9	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	16.8	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	12.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	11.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	10.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.5	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.3	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 Gemma 3 27B (27B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A74
Q3_K_S	3	13.2 GB	Low	A74
NVFP4	4	15.1 GB	Medium	A75
Q4_K_M	4	16.5 GB	Medium	A75
Q5_K_M	5	19.4 GB	High	A76
Q6_K	6	22.1 GB	High	A76
Q8_0Best for your GPU	8	28.9 GB	Very High	A78
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.6 35B A3B	35B	S	59.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	73.2 tok/s
Qwen 3.5 35B A3B	35B	S	64.7 tok/s
Qwen 3 32B	32B	S	26.1 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Gemma 3 27B?

Yes, NVIDIA A16 64GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 29.8 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 35.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 3 27B achieves approximately 29.8 tokens per second decode speed with a time-to-first-token of 6489ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on NVIDIA A16 64GB receives a A grade with 29.8 tok/s and 57K context.

What context window can Gemma 3 27B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 3 27B can safely use up to 57K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Gemma 3 27B

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