RTX 5060 Ti 16GBOpção econômica16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.36.8 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can gemma 3 12b it run on RTX 2080 Ti 11GB?
YES — With Offload
C52Usable
Estimated from fit model
gemma 3 12b it needs ~10.7 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~55 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 10.7 GB, 54.7 tok/s, Runs with offload
10.7 GB required11.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
54.7 tok/s
TTFT
3539 ms
Safe context
19K
Memory
10.7 GB / 11.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on RTX 2080 Ti 11GB
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: 54.7 tok/s decode · 3.5s TTFT (warm) · 137 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 54.7 tok/s | 1931 ms | 19K |
| Coding | C | Runs with offload | 54.7 tok/s | 3539 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.7 GB host RAM) | 32.2 tok/s | 8753 ms | 19K |
| Reasoning | C | Runs with offload | 54.7 tok/s | 4183 ms | 19K |
| RAG | C | Very compromised (needs ~0.7 GB host RAM) | 32.2 tok/s | 10941 ms | 19K |
Inference speed
gemma 3 12b it inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for gemma 3 12b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~164 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 | 164.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 104.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 94.4 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 89.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 87.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 76.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 63.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 60.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 54.2 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 41.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 41.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 32.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 32.5 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 30.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 25.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 11.0 | 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 3 12b it (12B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C53 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
NVFP4 | 4 | 6.7 GB | Medium | C52 |
Q4_K_MBest for your GPU | 4 | 7.3 GB | Medium | C52 |
Q5_K_M | 5 | 8.6 GB | High | F0 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run gemma 3 12b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server startOpções de upgrade
Hardware que roda bem gemma 3 12b it
RTX 5060 Ti 16GBOpção econômicaRTX 4060 Ti 16GBMelhor custo-benefício16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.30.2 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 12GBUpgrade NVIDIA12 GB VRAM (+1)672 GB/s (+56)
C
This setup is broadly balanced for this model.56.1 tok/s decodificação
~$549 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run gemma 3 12b it?
Yes, RTX 2080 Ti 11GB can run gemma 3 12b it with a C grade (Runs with offload). Expected decode speed: 54.7 tok/s.
How much VRAM does gemma 3 12b it need?
gemma 3 12b it (12B parameters) requires approximately 10.7 GB of memory with Q4_K_M quantization.
What is the best quantization for gemma 3 12b it?
The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.
What speed will gemma 3 12b it run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, gemma 3 12b it achieves approximately 54.7 tokens per second decode speed with a time-to-first-token of 3539ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run gemma 3 12b it for coding?
For coding workloads, gemma 3 12b it on RTX 2080 Ti 11GB receives a C grade with 54.7 tok/s and 19K context.
What context window can gemma 3 12b it use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, gemma 3 12b it can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if gemma 3 12b it feels slow on RTX 2080 Ti 11GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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