8GB VRAM: The Undisputed Minimum for Local AI
The days of squeezing functional AI into 6GB of video memory are effectively over.
As we enter Q1 2026, the local inference landscape has solidified around a single, brutal truth: 8GB of VRAM is the hard floor. Anything less is a compromise that fundamentally breaks the experience of modern reasoning models like DeepSeek-R1 and Llama-3.1.
The 6GB Trap
For years, the RTX 3050 and 4050 (6GB mobile variants) were touted as "good enough" for entry-level gaming. And for gaming, they often were. But neural networks don't scale like video games.
In gaming, running out of VRAM means lower texture quality or slightly worse frame times. In AI, running out of VRAM means falling back to system RAM, which is orders of magnitude slower.
When a model like Llama-3.1-8B (quantized to Q4_K_M) requires ~6.5GB of memory to load with a reasonable context window, a 6GB card is instantly disqualified. The model spills over into slower system memory, and generation speeds plummet from a snappy 40 tokens/second down to a glacial 2 tokens/second.
The 4060 Sweet Spot
This is why the NVIDIA RTX 4060 (8GB)—and its successor, the RTX 5060—have become the "People's Champion" of local inference.
At The Neural Lab, we tested this extensively with our Dell G15 Workhorse and Xenon Interceptor.
| GPU Model | VRAM | Llama 3.1 8B Speed | Status |
|---|---|---|---|
| RTX 4050 | 6GB | ~2 t/s (Offload) | ❌ Not Recommended |
| RTX 4060 | 8GB | 51.27 t/s | ✅ Recommended |
| RTX 5060 | 8GB | 48.51 t/s | ✅ Recommended |
The jump is binary. You either fit the model, or you don't.
Why 8GB Matters Now
It's not just about Llama 3. The new wave of "Reasoning Models" (like DeepSeek R1 and Qwen 2.5) rely heavily on Context Length to "think."
- Standard Chat: Uses ~2k context (Requires ~5.5GB VRAM)
- Reasoning / Coding: Uses ~8k-32k context (Requires 7GB+ VRAM)
An 8GB card allows you to run a quantized 8B model and have just enough heavy context window left to let the model actually reason through complex problems. A 6GB card chokes the moment you paste a large code block.
Conclusion
If you are buying a laptop for AI in 2026, do not let a salesperson talk you into a 6GB card to save $100. That $100 saving effectively costs you the ability to run 90% of modern open-source models.
The RTX 4060 Laptop remains the absolute best value in the ecosystem. It is the gatekeeper of local intelligence.