willitrun·ai

Can gemma 3 1b it run on RTX 4080 Super 16GB?

YES — Runs Great

C41Usable
Estimated from fit model

gemma 3 1b it needs ~3.2 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 3.2 GB, 16.0 tok/s, Runs well
3.2 GB required16.0 GB available
20% VRAM used

Fit status

Runs well

Decode

16.0 tok/s

TTFT

12100 ms

Safe context

1.8M

Memory

3.2 GB / 16.0 GB

Memory breakdown

Weights0.6 GB
KV Cache0.1 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsgemma 3 1b it on RTX 4080 Super 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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well16.0 tok/s6600 ms1.0M
CodingCRuns well16.0 tok/s12100 ms1.8M
Agentic CodingCRuns well16.0 tok/s17600 ms1.8M
ReasoningCRuns well16.0 tok/s14300 ms1.8M
RAGCRuns well16.0 tok/s22000 ms1.8M

Inference speed

gemma 3 1b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 3 1b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~19 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M19.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M16.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M16.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M14.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M14.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M14.0Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M14.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M14.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M14.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M14.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M14.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M14.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M14.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M14.0Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M14.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M14.0Fits

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 1b it (1B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.4 GB
LowC45
Q3_K_S
3
0.5 GB
LowC45
NVFP4
4
0.6 GB
MediumC45
Q4_K_M
4
0.6 GB
MediumC45
Q5_K_M
5
0.7 GB
HighC45
Q6_K
6
0.8 GB
HighC45
Q8_0
8
1.1 GB
Very HighC46
F16Best for your GPU
16
2.1 GB
MaximumC46

Get started

Copy-paste commands to run gemma 3 1b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-1b-it-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien gemma 3 1b it

MacBook Pro M4 32GBOpción económica
32 GB Unified (+16)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.14 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$799 MSRP

MacBook Pro M3 24GBMejor relación calidad-precio
24 GB Unified (+8)
C
Este setup está bastante equilibrado para este modelo.14 tok/s decodificación

~$1,099 MSRP

Frequently asked questions

Can RTX 4080 Super 16GB run gemma 3 1b it?

Yes, RTX 4080 Super 16GB can run gemma 3 1b it with a C grade (Runs well). Expected decode speed: 16.0 tok/s.

How much VRAM does gemma 3 1b it need?

gemma 3 1b it (1B parameters) requires approximately 3.2 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 1b it?

The recommended quantization for gemma 3 1b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 1b it run at on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, gemma 3 1b it achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12100ms using Q4_K_M quantization.

Can RTX 4080 Super 16GB run gemma 3 1b it for coding?

For coding workloads, gemma 3 1b it on RTX 4080 Super 16GB receives a C grade with 16.0 tok/s and 1.8M context.

What context window can gemma 3 1b it use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, gemma 3 1b it can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4080 Super 16GBSee all hardware for gemma 3 1b it
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