NVIDIA · Ampere · 16 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 3080 Ti Laptop GPU (16 GB)
Runs 14B models well; 27–31B only just fits.
16 GB GDDR6 at 16 Gbps (512 GB/s) on GA103 - fits ~13-14B Q4 models. Real performance depends on the chassis power limit.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 102 t/s | 102 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 110 t/s | 110 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 35.0 t/s | 35.0 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 40.4 t/s | 40.4 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 110 t/s | 110 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 62.8 t/s | 62.8 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 40.4 t/s | 40.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 242 t/s | 242 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 110 t/s | 110 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 60.3 t/s | 60.3 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 62.8 t/s | 62.8 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 40.4 t/s | 40.4 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 102 t/s | 102 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 41.2 t/s | 41.2 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 62.8 t/s | 62.8 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 40.4 t/s | 40.4 tok/s | Q4_K_M · 9.0 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | GPT OSS 20BOpenAIHow to run → Q4_K_M · 13.1 GB · 110 t/s | 110 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 102 t/s | 102 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 40.4 t/s | 40.4 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 35.0 t/s | 35.0 tok/s | Q4_K_M · 12.5 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 52.3 t/s | 52.3 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 102 t/s | 102 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 41.4 t/s | 41.4 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 117 t/s | 117 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 53.6 t/s | 53.6 tok/s | Q4_K_M · 6.5 GB |
The RTX 3080 Ti Laptop GPU runs 14B models well. Ampere generation, released 2022.
RTX 3080 Ti Laptop GPU strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 119 tok/s in 15.7 GB
- Low power draw: 150 W board power; a 300 W PSU is enough
Weaknesses
- 70B models don't fit: Llama 3.3 70B Instruct needs 51.3 GB at Q4 vs 16 GB here
- No FP8 or NVFP4: Ampere predates both; use GGUF, AWQ or GPTQ builds
Cards people weigh against the RTX 3080 Ti Laptop GPU, each with the one reason it is worth a look.
Output speed depends on memory bandwidth (512 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB52.3tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB35.0tok/s
- 22.2tok/s
- 119tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 1073 models on the RTX 3080 Ti Laptop GPU
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
InternVL3 5 8BVision | Q4_K_M | 8.2 GB | 59.1 t/s | — | A83, Runs well | |
Ternary Bonsai 8B UnpackedChatFunctions | Q4_K_M | 7.6 GB | 60.3 t/s | 66K | A84, Runs well | |
| Q4_K_M | 0.8 GB | 432.2 t/s | 131K | S100, Runs great | ||
VibeThinker 3BChatMathCodeReasoning | Q4_K_M | 2.8 GB | 149.2 t/s | 131K | S97, Runs great | |
Unlimited OCR 2Vision | Q4_K_M | 3.3 GB | 315.6 t/s | 131K | S100, Runs great | |
Gemma4 E4b Claims ComparisonChatFunctions | Q4_K_M | 5.5 GB | 63.0 t/s | 131K | A84, Runs well | |
| Q4_K_M | 0.7 GB | 594.3 t/s | 262K | S100, Runs great | ||
GPT OSS 20B NPU2ChatFunctions | Q4_K_M | 12.6 GB | 134.9 t/s | 131K | S95, Runs great | |
MiniCPM5 1BChatFunctions | Q4_K_M | 1.3 GB | 336.2 t/s | 131K | S100, Runs great | |
Diffusiongemma 26B A4B ITVisionFunctions | Q3_K_M | 13.6 GB | 118.4 t/s | 262K | S88, Runs great | |
Kimi VL A3B InstructVisionFunctions | Q4_K_M | 10.7 GB | 124.8 t/s | 131K | S95, Runs great | |
Qwen3 0.6BChatFunctions | Q4_K_M | 1.7 GB | 382.5 t/s | 41K | S100, Runs great | |
| Q4_K_M | 0.3 GB | 924.4 t/s | 1K | S100, Runs great | ||
GPT OSS 20B RichardErkhov HeresyChatFunctions | Q4_K_M | 13.1 GB | 109.8 t/s | 131K | S90, Runs great | |
HRM Text 1BChat | BF16 | 3.1 GB | 116.0 t/s | 4K | S94, Runs great |
Specs
- Architecture
- Ampere
- VRAM
- 16 GB GDDR6
- Memory bandwidth
- 512.0 GB/s
- FP16 (dense)
- 47.22 TFLOPS
- CUDA cores
- 7,424
- Tensor cores
- 232
- Compute capability
- 8.6
- TDP
- 150 W
- Released
- 2022-02-01
RTX 3080 Ti Laptop GPU FAQ
What is the largest model the RTX 3080 Ti Laptop GPU can run?
Gemma 4 26B A4B IT (25.8B) is the largest at 4-bit or better: at Q4_K_S it uses 15.5 GB of 16 GB, and runs at about 106 tok/s. At 3-bit, a bigger model fits too: Gemma 2 27B IT (27.2B) at IQ3_M, 15.0 GB.
Can the RTX 3080 Ti Laptop GPU run Qwen3.5 9B?
Yes. Qwen3.5 9B fits at Q4_K_M, using 6.6 GB of 16 GB, and it is the best model that runs well on this card.
Can the RTX 3080 Ti Laptop GPU run 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than the 16 GB on this card.
How many tokens per second does the RTX 3080 Ti Laptop GPU give?
About 52.3 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 3.7 s on a 4K-token prompt.
What power supply does the RTX 3080 Ti Laptop GPU need?
Plan a 300 W PSU (2× the 150 W TDP).
Is 16 GB enough for coding and agents?
Yes for coding: GPT OSS 20B, the best of the 2 coding models that fit, runs at about 110 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RTX 3080 Ti Laptop GPU?
An RTX 3090 (24 GB) rents from $0.18/hr. With regular daily use, owning pays off.