Inference on a Budget: You Don’t Need a 4090
There is a pervasive myth in the Local LLM community: "If you don't have 24GB of VRAM, don't bother."
We are told that unless you are rocking a dual-3090 setup or a Mac Studio M2 Ultra, you are stuck with "toy models" that can't reason.
The data from The Neural Lab says otherwise.
We recently put two "underdog" rigs to the test against Google's latest Gemma 3 (4B) model. The results didn't just surprise us; they proved that meaningful, high-speed intelligence is now accessible to almost anyone.
The Contenders
- "The Veteran": A glorious, dusty GTX 1080 (8GB). Released in 2016.
- "Mobile Command": An entry-level RTX 4050 Laptop GPU (6GB).
Both of these cards would be laughed out of a "Training" room. But for "Inference"? They are beasts.
The 50 t/s Threshold
Human reading speed is roughly 5-10 tokens per second. If an agent generates text faster than that, it feels "instant."
Here is what our budget rigs achieved on Gemma 3 (Q4_K_M):
- GTX 1080: 46.29 t/s
- RTX 4050: 58.17 t/s
Think about that. A GPU that predates the Transformer architecture (the 1080) is generating thought 4x faster than you can read it.
The VRAM Miracle
"But what about context?" you ask. "Doesn't it crash at 6GB?"
Not anymore. Thanks to modern optimizations in llama.cpp and hybrid memory offloading, we ran full 64k context tests on both cards.
- The RTX 4050 (6GB) benefited from the laptop's "Hybrid Mode," letting the iGPU handle the display. This kept the VRAM clear, allowing the entire 64k context to fit (using 5GB/6GB) and maintaining 41.91 t/s without touching system RAM.
- The GTX 1080 held strong at 36.82 t/s.
The Conclusion
The hardware barrier is gone.
You don't need a $4,000 workstation to run an agent that can summarize documents, write code, or roleplay. You might just need that old gaming PC collecting dust in your closet.
Intelligence is becoming efficient faster than hardware is becoming obsolete. And that is a very bullish signal for the future.