Can gemma 3 27b it run on NVIDIA A16 64GB?

YES — Runs Great

C47Usable

Estimated from fit model

gemma 3 27b it needs ~27.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~28 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) — 27.2 GB, 28.4 tok/s, Runs well

27.2 GB required64.0 GB available

43% VRAM used

Fit status

Runs well

Decode

28.4 tok/s

TTFT

6813 ms

Safe context

202K

Memory

27.2 GB / 64.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on NVIDIA A16 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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	C	Runs well	28.4 tok/s	3716 ms	202K
Coding	C	Runs well	28.4 tok/s	6813 ms	202K
Agentic Coding	C	Runs well	28.4 tok/s	9910 ms	202K
Reasoning	C	Runs well	28.4 tok/s	8052 ms	202K
RAG	C	Runs well	28.4 tok/s	12388 ms	202K

Quantization options

How gemma 3 27b it (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	C41
Q3_K_S	3	13.2 GB	Low	C42
NVFP4	4	15.1 GB	Medium	C42
Q4_K_M	4	16.5 GB	Medium	C43
Q5_K_M	5	19.4 GB	High	C43
Q6_K	6	22.1 GB	High	C44
Q8_0Best for your GPU	8	28.9 GB	Very High	C46
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server start

アップグレードオプション

gemma 3 27b itを快適に動かすハードウェア

RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.91.4 tok/sデコード

Raises estimated decode speed by about 222%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 187%.81.4 tok/sデコード

Raises estimated decode speed by about 187%.

Adds memory headroom for longer context windows and future model growth.

〜$9,999 MSRP

NVIDIA H20 96GBNVIDIAアップグレード

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 593%.196.7 tok/sデコード

Raises estimated decode speed by about 593%.

Adds memory headroom for longer context windows and future model growth.

〜$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run gemma 3 27b it?

Yes, NVIDIA A16 64GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 28.4 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 27.2 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 27b it?

The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 27b it run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, gemma 3 27b it achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6813ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on NVIDIA A16 64GB receives a C grade with 28.4 tok/s and 202K context.

What context window can gemma 3 27b it use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, gemma 3 27b it can safely use up to 202K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for gemma 3 27b it

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--gemma-3-27b-it-gguf-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: