Can Gemma 2 9B run on NVIDIA A16 64GB?

YES — Runs Great

B62Good

Estimated from fit model

Gemma 2 9B needs ~18.2 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~90 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) — 18.2 GB, 89.5 tok/s, Runs well

18.2 GB required64.0 GB available

28% VRAM used

Fit status

Runs well

Decode

89.5 tok/s

TTFT

2163 ms

Safe context

Memory

18.2 GB / 64.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsGemma 2 9B 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: 89.5 tok/s decode · 2.2s TTFT (warm) · 224 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	B	Runs well	89.5 tok/s	1180 ms	8K
Coding	B	Runs well	89.5 tok/s	2163 ms	8K
Agentic Coding	B	Runs well	89.5 tok/s	3146 ms	8K
Reasoning	B	Runs well	89.5 tok/s	2556 ms	8K
RAG	B	Runs well	89.5 tok/s	3933 ms	8K

Quantization options

How Gemma 2 9B (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C55
Q3_K_S	3	4.4 GB	Low	C55
NVFP4	4	5.0 GB	Medium	C55
Q4_K_M	4	5.5 GB	Medium	B55
Q5_K_M	5	6.5 GB	High	B55
Q6_K	6	7.4 GB	High	B55
Q8_0	8	9.6 GB	Very High	B56
F16Best for your GPU	16	18.5 GB	Maximum	B57

Get started

Copy-paste commands to run Gemma 2 9B on your machine.

Run

ollama run gemma2

Upgrade options

Hardware that runs Gemma 2 9B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.71.7 tok/s decode

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+219)

This setup is broadly balanced for this model.106.5 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Gemma 2 9B?

Yes, NVIDIA A16 64GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 89.5 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 18.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 2 9B achieves approximately 89.5 tokens per second decode speed with a time-to-first-token of 2163ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on NVIDIA A16 64GB receives a B grade with 89.5 tok/s and 8K context.

What context window can Gemma 2 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Gemma 2 9B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

89.5 tok/s

1180 ms

Coding

Runs well

89.5 tok/s

2163 ms

Agentic Coding

Runs well

89.5 tok/s

3146 ms

Reasoning

Runs well

89.5 tok/s

2556 ms

RAG

Runs well

89.5 tok/s

3933 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.5 GB

Low

C55

Q3_K_S

4.4 GB

Low

C55

NVFP4

5.0 GB

Medium

C55

Q4_K_M

5.5 GB

Medium

B55

Q5_K_M

6.5 GB

High

B55

Q6_K

7.4 GB

High

B55

Q8_0

9.6 GB

Very High

B56

F16Best for your GPU

18.5 GB

Maximum

B57

Frequently asked questions

Can NVIDIA A16 64GB run Gemma 2 9B?

Yes, NVIDIA A16 64GB can run Gemma 2 9B with a B grade (Runs well). Expected decode speed: 89.5 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 18.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 9B?

The recommended quantization for Gemma 2 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 9B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 2 9B achieves approximately 89.5 tokens per second decode speed with a time-to-first-token of 2163ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on NVIDIA A16 64GB receives a B grade with 89.5 tok/s and 8K context.

What context window can Gemma 2 9B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Gemma 2 9B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.