MacBook Pro M4 16GBOpção econômica
16 GB Unified (+6)
C
This setup is broadly balanced for this model.14 tok/s decodificação
~$599 MSRP
Can gemma 3 1b it run on RTX 3080 10GB?
YES — Runs Great
C42Usable
Estimated from fit model
gemma 3 1b it needs ~2.9 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~14 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.
Select quantization to explore
Q4_K_M (Medium quality) — 2.9 GB, 14.0 tok/s, Runs well
2.9 GB required10.0 GB available
29% VRAM used
Fit status
Runs well
Decode
14.0 tok/s
TTFT
13829 ms
Safe context
982K
Memory
2.9 GB / 10.0 GB
Memory breakdown
Weights0.6 GB
KV Cache0.1 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsgemma 3 1b it on RTX 3080 10GB
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: 14.0 tok/s decode · 13.8s TTFT (warm) · 35 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 | 14.0 tok/s | 7543 ms | 575K |
| Coding | C | Runs well | 14.0 tok/s | 13829 ms | 982K |
| Agentic Coding | C | Runs well | 14.0 tok/s | 20114 ms | 982K |
| Reasoning | C | Runs well | 14.0 tok/s | 16343 ms | 982K |
| RAG | C | Runs well | 14.0 tok/s | 25143 ms | 982K |
Quantization options
How gemma 3 1b it (1B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C48 |
Q3_K_S | 3 | 0.5 GB | Low | C48 |
NVFP4 | 4 | 0.6 GB | Medium | C48 |
Q4_K_M | 4 | 0.6 GB | Medium | C48 |
Q5_K_M | 5 | 0.7 GB | High | C48 |
Q6_K | 6 | 0.8 GB | High | C48 |
Q8_0 | 8 | 1.1 GB | Very High | C48 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | C50 |
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 startOpções de upgrade
Hardware que roda bem gemma 3 1b it
Frequently asked questions
Can RTX 3080 10GB run gemma 3 1b it?
Yes, RTX 3080 10GB can run gemma 3 1b it with a C grade (Runs well). Expected decode speed: 14.0 tok/s.
How much VRAM does gemma 3 1b it need?
gemma 3 1b it (1B parameters) requires approximately 2.9 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 3080 10GB?
On RTX 3080 10GB, gemma 3 1b it achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q4_K_M quantization.
Can RTX 3080 10GB run gemma 3 1b it for coding?
For coding workloads, gemma 3 1b it on RTX 3080 10GB receives a C grade with 14.0 tok/s and 982K context.
What context window can gemma 3 1b it use on RTX 3080 10GB?
On RTX 3080 10GB, gemma 3 1b it can safely use up to 982K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--gemma-3-1b-it-gguf-on-rtx-3080-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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