Can Gemma 2 9B run on RTX 5080 Laptop 16GB?

YES — Runs Great

A71Great

Estimated from fit model

Gemma 2 9B needs ~13.1 GB VRAM. RTX 5080 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~94 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: 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) — 13.1 GB, 93.5 tok/s, Runs well

13.1 GB required16.0 GB available

82% VRAM used

Fit status

Runs well

Decode

93.5 tok/s

TTFT

2070 ms

Safe context

Memory

13.1 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache5.1 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGemma 2 9B on RTX 5080 Laptop 16GB
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: 93.5 tok/s decode · 2.1s TTFT (warm) · 234 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	93.5 tok/s	1129 ms	8K
Coding	A	Runs well	93.5 tok/s	2070 ms	8K
Agentic Coding	B	Very compromised (needs ~0.7 GB host RAM)	53.2 tok/s	5293 ms	8K
Reasoning	A	Runs well	93.5 tok/s	2447 ms	8K
RAG	B	Very compromised (needs ~0.7 GB host RAM)	53.2 tok/s	6616 ms	8K

Inference speed

Gemma 2 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 2 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	125.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	86.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	80.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	78.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	71.7	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	67.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	63.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	54.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	45.9	Fits
RTX 4070 12GB	12 GB	Q4_K_M	45.7	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	42.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	37.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	28.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.9	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 Gemma 2 9B (9B params) fits at each quantization level on RTX 5080 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B62
Q3_K_S	3	4.4 GB	Low	B63
NVFP4	4	5.0 GB	Medium	B63
Q4_K_M	4	5.5 GB	Medium	B64
Q5_K_M	5	6.5 GB	High	B65
Q6_K	6	7.4 GB	High	B66
Q8_0Best for your GPU	8	9.6 GB	Very High	B66
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

ollama run gemma2

Your hardware

More models your RTX 5080 Laptop 16GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	S	81.6 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	77.3 tok/s
GPT-OSS 20B	21B	A	74.6 tok/s
Ministral 3 14B	14B	S	81.2 tok/s
Codestral 2 25.08	22B	A	25.9 tok/s

Frequently asked questions

Can RTX 5080 Laptop 16GB run Gemma 2 9B?

Yes, RTX 5080 Laptop 16GB can run Gemma 2 9B with a A grade (Runs well). Expected decode speed: 93.5 tok/s.

How much VRAM does Gemma 2 9B need?

Gemma 2 9B (9B parameters) requires approximately 13.1 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 RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, Gemma 2 9B achieves approximately 93.5 tokens per second decode speed with a time-to-first-token of 2070ms using Q4_K_M quantization.

Can RTX 5080 Laptop 16GB run Gemma 2 9B for coding?

For coding workloads, Gemma 2 9B on RTX 5080 Laptop 16GB receives a A grade with 93.5 tok/s and 8K context.

What context window can Gemma 2 9B use on RTX 5080 Laptop 16GB?

On RTX 5080 Laptop 16GB, 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 RTX 5080 Laptop 16GB See all hardware for Gemma 2 9B

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