What can you run on a 16GB graphics card?
With 16GB VRAM you can run 9 of the 30 open-source models we track — up to about 22B parameters at Q4 quantization. The most popular that fits is Qwen3.5-9B, needing 6.8 GB and generating around ~82 tok/s on typical hardware of this size.
Typical machines: RTX 4080 · RTX 5080 · RTX 4060 Ti 16GB · RX 7800 XT · Arc A770. After the operating system takes its share, about 16 GB is free for a model.
Having the memory is not the same as having the speed
16GB VRAM decides whether a model loads. How fast it answers is decided by memory bandwidth — how quickly your machine can read the model, which it must do once for every word it writes. Machines with 16GB VRAM span 288–960 GB/s, so the fastest is about 3.3× quicker than the slowest with identical capacity. The table quotes a RTX 4080; pick yours below to see the difference. Why bandwidth and not the processor →
Models that run on 16GB VRAM in 2026
| Model | Size | Memory | Est. speed | Feels like |
|---|---|---|---|---|
| Qwen3.5-9BAlibaba / Qwen | 9.7B | 6.8 GBRuns well | ~82 tok/s | Fast |
| gpt-oss-20bOpenAI · MoE | 22B | 13 GBRuns well | ~200 tok/s4.3B active | Fast |
| Qwen3.5-4BAlibaba / Qwen | 4.7B | 3.8 GBRuns well | ~170 tok/s | Fast |
| Qwen3.5-0.8BAlibaba / Qwen | 873M | 1.3 GBRuns well | ~875 tok/s | Fast |
| Qwen3.5-2BAlibaba / Qwen | 2.3B | 2.0 GBRuns well | ~364 tok/s | Fast |
| Olmo-3-7B-InstructAi2 | 7.3B | 7.2 GBRuns well | ~104 tok/s | Fast |
| GLM-4.6V-FlashZhipu AI | 10B | 7.0 GBRuns well | ~76 tok/s | Fast |
| ERNIE-4.5-21B-A3B-PTBaidu AI · MoE | 22B | 15 GBTight | ~35 tok/s | Fast |
| ERNIE-4.5-0.3B-PTBaidu AI | 361M | 853 MBRuns well | ~1937 tok/s | Fast |
All figures at Q4_K_M quantization, 4K context. “Memory” includes the model, its context cache and runtime overhead. Speed is calculated, not benchmarked: 717 GB/s × 0.65 ÷ bytes read per token, assuming a RTX 4080. Scale it by your own machine's bandwidth from the list above. Mixture-of-experts models read only their active parameters per token, which is why some large ones outrun smaller dense models.
Image generators that also fit
These make pictures rather than text. Their memory use scales with the resolution you generate at, not conversation length, so treat these figures as a floor rather than a ceiling.
What you'd need more memory for
The next models up, and what they ask for.