Guides / 16 GB GPU
Best LLMs that run on a 16 GB GPU
Updated November 2026
As of November 2026, the best option is Nanbeige4.2 3B by Nanbeige (Kanzhun), with a Small LM Index of 70.4, followed by Spark-X2.6 4B (67.5) and Spark-X2.5 4B (65.5).
Models that fit in a 16 GB graphics card at 4-bit quantization with an 8K-token context, ranked by the Small LM Index. Try other hardware and context lengths in the Can I run it? tool.
| # | Model | Lab | Small LM Index | Params | Memory (4-bit) | API / 1M | License |
|---|---|---|---|---|---|---|---|
| 1 | Nanbeige4.2 3B | Nanbeige (Kanzhun) | 70.4 | 4.2B | ~3.8 GB | self-host | Apache-2.0 |
| 2 | Spark-X2.6 4B | SparkLLM (XHToken) | 67.5 | 4.1B | ~3.4 GB | self-host | Apache-2.0 |
| 3 | Spark-X2.5 4B | SparkLLM (XHToken) | 65.5 | 4.1B | ~3.4 GB | self-host | Apache-2.0 |
| 4 | gpt-oss-20b | OpenAI | 64.0 | 20.9B (3.6B active) | ~13.8 GB | $0.020 | Apache-2.0 |
| 5 | Qwen3.5 9B | Alibaba (Qwen) | 63.9 | 9B | ~6.4 GB | $0.028 | Apache-2.0 |
| 6 | Granite 4.2 8B | IBM (Granite) | 63.5 | 8B | ~6.9 GB | $0.082 | Apache-2.0 |
| 7 | Apriel 1.6 15B Thinker | ServiceNow (Apriel) | 60.9 | 14.9B | ~11.4 GB | self-host | MIT |
| 8 | LFM3 1.7B | Liquid AI | 60.0 | 1.7B | ~1.7 GB | self-host | LFM Open License |
| 9 | Nemotron Labs Dory 1.0 4B | NVIDIA | 60.0 | 4B | ~3.4 GB | self-host | OpenMDW-1.1 |
| 10 | Ministral 3 14B | Mistral AI | 60.0 | 14B | ~10.6 GB | $0.20 | Apache-2.0 |
| 11 | Qwen3.5 4B | Alibaba (Qwen) | 57.8 | 4B | ~3.3 GB | self-host | Apache-2.0 |
| 12 | MiniCPM5 2B | OpenBMB (MiniCPM) | 57.5 | 2.5B | ~2.4 GB | self-host | Apache-2.0 |
| 13 | Falcon H1R 7B | TII (Falcon) | 57.5 | 7.6B | ~5.6 GB | self-host | Falcon LLM License |
| 14 | Gemma 4 12B | 56.9 | 11.9B | ~8.9 GB | self-host | Apache-2.0 | |
| 15 | Qwen3 4B Thinking 2507 | Alibaba (Qwen) | 55.7 | 4B | ~4.2 GB | self-host | Apache-2.0 |
Memory covers the weights only; long contexts need more. Scores are mostly lab-reported, so gaps of 1–2 points are noise. See the methodology, or filter the full leaderboard yourself.
Head to head
Questions
What is the best LLM for a 16 GB graphics card?
As of November 2026, the best option is Nanbeige4.2 3B by Nanbeige (Kanzhun), with a Small LM Index of 70.4, followed by Spark-X2.6 4B (67.5) and Spark-X2.5 4B (65.5).
How is this list ranked?
Models that fit in a 16 GB graphics card at 4-bit quantization with an 8K-token context, ranked by the Small LM Index.
Which of the top five needs the least memory?
Spark-X2.6 4B, at about 3.4 GB with 4-bit quantization.
Which is the cheapest to use through an API?
gpt-oss-20b, at $0.020 per million tokens from the cheapest provider on OpenRouter.
Other guides
- Best small open models for coding
- Best small open models for math
- Best LLMs that run on an 8 GB GPU or laptop
- Best LLMs for a 24 GB GPU (RTX 4090, RTX 3090)
- Best tiny LLMs for phones and edge devices
- Best cheap LLM APIs (open models)
- Best small open vision-language models
- Best open models with an Apache 2.0 or MIT license
- Best European open language models
- Best American open models under 100B
- Best fully open models (open data and code)