RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+856)
C
Raises estimated decode speed by about 58%.19 tok/s 解码
Raises estimated decode speed by about 58%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Gemma 3 1B run on RTX 3090 24GB?
YES — Runs Great
C47Usable
Estimated from fit model
Gemma 3 1B needs ~4.3 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 4.3 GB, 12.0 tok/s, Runs well
4.3 GB required24.0 GB available
18% VRAM used
Fit status
Runs well
Decode
12.0 tok/s
TTFT
16133 ms
Safe context
33K
Memory
4.3 GB / 24.0 GB
Memory breakdown
Weights0.6 GB
KV Cache0.4 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsGemma 3 1B on RTX 3090 24GB
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: 12.0 tok/s decode · 16.1s TTFT (warm) · 30 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 | 12.0 tok/s | 8800 ms | 33K |
| Coding | C | Runs well | 12.0 tok/s | 16133 ms | 33K |
| Agentic Coding | C | Runs well | 12.0 tok/s | 23467 ms | 33K |
| Reasoning | C | Runs well | 12.0 tok/s | 19067 ms | 33K |
| RAG | C | Runs well | 12.0 tok/s | 29333 ms | 33K |
Quantization options
How Gemma 3 1B (1B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C52 |
Q3_K_S | 3 | 0.5 GB | Low | C52 |
NVFP4 | 4 | 0.6 GB | Medium | C52 |
Q4_K_M | 4 | 0.6 GB | Medium | C52 |
Q5_K_M | 5 | 0.7 GB | High | C52 |
Q6_K | 6 | 0.8 GB | High | C52 |
Q8_0 | 8 | 1.1 GB | Very High | C52 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | C52 |
Get started
Copy-paste commands to run Gemma 3 1B on your machine.
Run
lms load gemma-3-1b-it && lms server start升级选项
能流畅运行 Gemma 3 1B 的硬件
RTX 5090 32GB经济之选RTX PRO 4500 Blackwell 32GB最佳性价比32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.14 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.14 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP
Frequently asked questions
Can RTX 3090 24GB run Gemma 3 1B?
Yes, RTX 3090 24GB can run Gemma 3 1B with a C grade (Runs well). Expected decode speed: 12.0 tok/s.
How much VRAM does Gemma 3 1B need?
Gemma 3 1B (1B parameters) requires approximately 4.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 3 1B?
The recommended quantization for Gemma 3 1B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 3 1B run at on RTX 3090 24GB?
On RTX 3090 24GB, Gemma 3 1B achieves approximately 12.0 tokens per second decode speed with a time-to-first-token of 16133ms using Q4_K_M quantization.
Can RTX 3090 24GB run Gemma 3 1B for coding?
For coding workloads, Gemma 3 1B on RTX 3090 24GB receives a C grade with 12.0 tok/s and 33K context.
What context window can Gemma 3 1B use on RTX 3090 24GB?
On RTX 3090 24GB, Gemma 3 1B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.
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