NVIDIA · Ada Lovelace · 16 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 4090 Laptop GPU (16 GB)
Runs 14B models well; 27–31B only just fits.
Laptop GPU is AD103 with 16 GB — NOT the desktop RTX 4090's 24 GB. Performance varies with the chassis power limit.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 58.9 t/s | 58.9 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 115 t/s | 115 tok/s | Q4_K_M · 3.4 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 121 t/s | 121 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Phi 4Microsoft Q4_K_M · 12.5 GB · 39.3 t/s | 39.3 tok/s | Q4_K_M · 12.5 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 45.5 t/s | 45.5 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 · 121 t/s | 121 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Q4_K_M · 7.9 GB · 70.6 t/s | 70.6 tok/s | Q4_K_M · 7.9 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 58.9 t/s | 58.9 tok/s | overall | Q4_K_M · 6.6 GB |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 45.5 t/s | 45.5 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 262 t/s | 262 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 · 58.9 t/s | 58.9 tok/s | Q4_K_M · 6.6 GB | |
| 2 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 121 t/s | 121 tok/s | Q4_K_M · 13.1 GB | |
| 3 | Qwen3 8BAlibaba Q4_K_M · 7.6 GB · 67.8 t/s | 67.8 tok/s | Q4_K_M · 7.6 GB | |
| 4 | Q4_K_M · 7.9 GB · 70.6 t/s | 70.6 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 45.5 t/s | 45.5 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 · 58.9 t/s | 58.9 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 115 t/s | 115 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Mistral Nemo Instruct 2407Mistral AI Q4_K_M · 11.0 GB · 46.4 t/s | 46.4 tok/s | Q4_K_M · 11.0 GB | |
| 4 | Q4_K_M · 7.9 GB · 70.6 t/s | 70.6 tok/s | Q4_K_M · 7.9 GB | |
| 5 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 45.5 t/s | 45.5 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 · 121 t/s | 121 tok/s | Q4_K_M · 13.1 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 115 t/s | 115 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 58.9 t/s | 58.9 tok/s | Q4_K_M · 6.6 GB | |
| 4 | Gemma 4 12B ITGoogle Q4_K_M · 9.0 GB · 45.5 t/s | 45.5 tok/s | Q4_K_M · 9.0 GB | |
| 5 | Phi 4Microsoft Q4_K_M · 12.5 GB · 39.3 t/s | 39.3 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 · 58.9 t/s | 58.9 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 115 t/s | 115 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 12B ITGoogle Q4_K_M · 10.9 GB · 46.6 t/s | 46.6 tok/s | Q4_K_M · 10.9 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 132 t/s | 132 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Q4_K_M · 6.5 GB · 60.3 t/s | 60.3 tok/s | Q4_K_M · 6.5 GB |
The RTX 4090 Laptop GPU runs 14B models well. Ada Lovelace generation, released 2023.
RTX 4090 Laptop GPU strengths and weaknesses
Strengths
- Runs 35B MoE models: Qwen3.6 35B A3B at 130 tok/s in 15.7 GB
- Reads long prompts fast: A 4K-token prompt on Qwen3.5 9B starts answering in 2.2 s
- 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
Cards people weigh against the RTX 4090 Laptop GPU, each with the one reason it is worth a look.
Output speed depends on memory bandwidth (576 GB/s). Grey rows need more than 16 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB58.9tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB39.4tok/s
- 25.0tok/s
- 130tok/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 1057 models on the RTX 4090 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 |
|---|---|---|---|---|---|---|
| Q4_K_M | 6.8 GB | 76.1 t/s | 33K | S87, Runs great | ||
Turkish Gemma 9B T1ChatReasoning | Q4_K_M | 7.7 GB | 58.0 t/s | 8K | A83, Runs well | |
| Q4_K_M | 1.0 GB | 370.7 t/s | 2K | S100, Runs great | ||
Gemma3 1B ITChat | BF16 | 3.6 GB | 170.2 t/s | — | S98, Runs great | |
| Q4_K_M | 3.6 GB | 216.4 t/s | — | S100, Runs great | ||
| Q4_K_M | 4.2 GB | 129.1 t/s | 41K | S95, Runs great | ||
| Q4_K_M | 11.9 GB | 36.7 t/s | 4K | A73, Runs well | ||
Codeguard Jev Style 1.5BChatCodeFunctions | Q4_K_M | 1.7 GB | 292.5 t/s | 33K | S100, Runs great | |
H2ovl Mississippi 2BChatVision | BF16 | 6.5 GB | 79.0 t/s | — | S87, Runs great | |
ReaderLM v2Chat | Q4_K_M | 1.7 GB | 292.5 t/s | 513K | S100, Runs great | |
| Q4_K_M | 1.4 GB | 520.0 t/s | 262K | S100, Runs great | ||
Arch Router 1.5BChat | Q4_K_M | 1.7 GB | 292.5 t/s | 33K | S100, Runs great | |
NVIDIA Nemotron 3 Nano 4B BF16ChatFunctions | Q4_K_M | 2.9 GB | 129.1 t/s | 262K | S95, Runs great | |
TIPO 500M FtChat | Q4_K_M | 0.8 GB | 462.2 t/s | 2K | S100, Runs great | |
OpenMath Nemotron 1.5BChatMath | Q4_K_M | 1.7 GB | 292.5 t/s | 131K | S100, Runs great |
Specs
- Architecture
- Ada Lovelace
- VRAM
- 16 GB GDDR6
- Memory bandwidth
- 576.0 GB/s
- FP16 (dense)
- 79.40 TFLOPS
- CUDA cores
- 9,728
- Tensor cores
- 304
- Compute capability
- 8.9
- TDP
- 150 W
- Released
- 2023-02-08
RTX 4090 Laptop GPU FAQ
What is the largest model the RTX 4090 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 117 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 4090 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 4090 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 4090 Laptop GPU give?
About 58.9 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 2.2 s on a 4K-token prompt.
What power supply does the RTX 4090 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 121 tok/s. For agents, not yet: no agent-scored model fits in less than 96 GB.
Is renting cheaper than buying an RTX 4090 Laptop GPU?
An RTX 3090 Ti (24 GB) rents from $0.24/hr. With regular daily use, owning pays off.