NVIDIA · Blackwell · 8 GB VRAM tier
Best AI Models for NVIDIA GeForce RTX 5050 Laptop GPU (8 GB)
Runs 7–9B models well; 14B doesn't fit in 8 GB.
8 GB GDDR7 on a 128-bit bus: ~7-8B Q4 models at most. Speed varies with the chassis power limit.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 39.2 t/s | 39.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 76.6 t/s | 76.6 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 40.9 t/s | 40.9 tok/s | Q4_K_M · 7.3 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 87.9 t/s | 87.9 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 197 t/s | 197 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 · 39.2 t/s | 39.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 87.9 t/s | 87.9 tok/s | Q4_K_M · 5.0 GB | |
| 3 | Qwen2.5 7B InstructAlibaba Q4_K_M · 5.8 GB · 50.0 t/s | 50.0 tok/s | Q4_K_M · 5.8 GB | |
| 4 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 40.9 t/s | 40.9 tok/s | overall | Q4_K_M · 7.3 GB |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 197 t/s | 197 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 76.6 t/s | 76.6 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 39.2 t/s | 39.2 tok/s | Q4_K_M · 6.6 GB | |
| 3 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 46.9 t/s | 46.9 tok/s | Q4_K_M · 5.6 GB | |
| 4 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 197 t/s | 197 tok/s | Q4_K_M · 5.5 GB | |
| 5 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 84.3 t/s | 84.3 tok/s | Q4_K_M · 4.3 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 76.6 t/s | 76.6 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 197 t/s | 197 tok/s | Q4_K_M · 5.5 GB | |
| 3 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 46.9 t/s | 46.9 tok/s | Q4_K_M · 5.6 GB | |
| 4 | Q4_K_M · 6.5 GB · 40.2 t/s | 40.2 tok/s | Q4_K_M · 6.5 GB | |
| 5 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 39.2 t/s | 39.2 tok/s | Q4_K_M · 6.6 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 39.2 t/s | 39.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 76.6 t/s | 76.6 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 87.9 t/s | 87.9 tok/s | Q4_K_M · 5.0 GB | |
| 4 | Q4_K_M · 6.5 GB · 40.2 t/s | 40.2 tok/s | Q4_K_M · 6.5 GB | |
| 5 | MiniCPM V 4.7 1BOpenBMB Q4_K_M · 1.6 GB · 195 t/s | 195 tok/s | Q4_K_M · 1.6 GB |
The RTX 5050 Laptop GPU runs 7–9B models well. Blackwell generation, released 2025.
RTX 5050 Laptop GPU strengths and weaknesses
Strengths
- Runs 7–9B models: Qwen3.5 9B at 39.2 tok/s in 6.6 GB
- Low power draw: 100 W board power; a 200 W PSU is enough
Weaknesses
- 14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 8 GB here
- Little room for context: Qwen3.5 9B uses 6.6 of 8 GB, 1.4 GB left
- Slow prompt reading: A 4K-token prompt on Qwen3.5 9B waits 6.5 s for the first token
Cards people weigh against the RTX 5050 Laptop GPU, each with the one reason it is worth a look.
Output speed depends on memory bandwidth (384 GB/s). Grey rows need more than 8 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB39.2tok/s
- 14BQwen3 14B · not at 4-bitneeds 11.9 GB
- 27–31BQwen3.8 27B · not at 4-bitneeds 17.9 GB
- 35B MoEQwen3.6 35B A3B · not at 4-bitneeds 22.0 GB
- 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 842 models on the RTX 5050 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 | 7.7 GB | 38.6 t/s | 8K | B50, Decent | ||
| Q4_K_M | 6.3 GB | 49.5 t/s | 262K | A79, Runs well | ||
Kumru 2B BaseChat | Q4_K_M | 2.2 GB | 135.7 t/s | 8K | S96, Runs great | |
Qwen3 0.6B BaseChat | Q4_K_M | 1.6 GB | 320.0 t/s | 33K | S100, Runs great | |
Mellum2 12B A2.5B ThinkingChatFunctions | Q4_K_M | 7.8 GB | 129.9 t/s | 131K | B62, Decent | |
| Q4_K_M | 0.3 GB | 24960.0 t/s | — | S100, Runs great | ||
Qwen2.5 7B Instruct 1MChatFunctions | Q4_K_M | 5.8 GB | 50.0 t/s | 1010K | A79, Runs well | |
Medgemma 4B ITVision | Q4_K_M | 5 GB | 87.9 t/s | — | S89, Runs great | |
Qwen3 VL 8B ThinkingVisionFunctions | Q4_K_M | 8.0 GB | 42.6 t/s | 262K | C44, Usable | |
Mistral 7B v0.3Chat | Q4_K_M | 6.8 GB | 50.7 t/s | 33K | A73, Runs well | |
DeepSeek R1 Distill Llama 8BChatReasoningFunctions | Q4_K_M | 7.3 GB | 46.3 t/s | 131K | B62, Decent | |
| Q4_K_M | 0.4 GB | 832.0 t/s | 8K | S100, Runs great | ||
Qwen3 4B BaseChat | Q4_K_M | 4.2 GB | 86.1 t/s | 33K | S88, Runs great | |
Llama 3.1 8BChat | Q4_K_M | 7.9 GB | 47.1 t/s | — | C44, Usable | |
MiMo 7B BaseChat | Q4_K_M | 7.4 GB | 47.1 t/s | 33K | B59, Decent |
Specs
- Architecture
- Blackwell
- VRAM
- 8 GB GDDR7
- Memory bandwidth
- 384.0 GB/s
- FP16 (dense)
- 27.26 TFLOPS
- CUDA cores
- 2,560
- Tensor cores
- 80
- Compute capability
- 12.0
- TDP
- 100 W
- Released
- 2025-06-01
RTX 5050 Laptop GPU FAQ
What is the largest model the RTX 5050 Laptop GPU can run?
Qwen3.5 9B (9.7B) is the largest at 4-bit or better: at Q4_K_M it uses 6.6 GB of 8 GB, and runs at about 39.2 tok/s.
Can the RTX 5050 Laptop GPU run 14B models?
No. Qwen3 14B needs 11.9 GB at Q4, more than the 8 GB on this card.
How many tokens per second does the RTX 5050 Laptop GPU give?
About 39.2 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 6.4 s on a 4K-token prompt.
What power supply does the RTX 5050 Laptop GPU need?
Plan a 200 W PSU (2× the 100 W TDP).
Is renting cheaper than buying an RTX 5050 Laptop GPU?
An RTX 3090 (24 GB) rents from $0.18/hr. With regular daily use, owning pays off.