A number worth remembering surfaced this week on Reddit's LocalLLaMA: 65 tokens/s. A developer used a custom inference engine to run Alibaba's open-source Qwen3.8-Flash-Next model on a 12GB RTX 5070, achieving 65 tokens/s output and 430+ tokens/s read speed. That's over 10x faster than average human reading speed (4-5 tokens/s).
This is one of the most signal-rich localization breakthroughs we've seen this week. Not because of the number 65 itself, but because 12GB of VRAM is the standard configuration in many existing laptops and workstations. Once this threshold is crossed, "running a usable open-source LLM locally" shifts from a hobbyist toy to a viable option for ordinary knowledge workers.
What this is
The developer wrote a custom inference engine specifically optimized for the Qwen3.8 model (the program that actually runs the model and generates text), paired with RCO-GSQ, a 2-bit quantization technique (quantization = compressing model parameters so smaller GPUs can handle them). This crammed a model that typically requires professional GPUs onto a gaming GPU priced at ¥4,000-5,000 (~$550-700). It's open-source, one-click install, currently NVIDIA-only.
It validates a path: open weights + dedicated community optimization + consumer hardware. The cost curve for local AI is dropping fast.
Industry view
Bulls read it directly: Chinese open-source models stacked with global community optimization mean local AI's cost decline is steeper than cloud APIs. A ¥5,000 (~$700) GPU plus 64GB RAM can deliver a near-cloud experience — meaningful for SMBs sensitive to private deployment.
But we noted two counterarguments. First, the 65 tokens/s is achieved under extreme Q2_0 quantization, with clearly degraded response quality compared to the original — "can run" doesn't equal "runs well." Second, the engineering model of "one person writing a custom engine for one model" is fragile; the developer's personal bandwidth determines whether it stays maintained. Cloud APIs still hold a stability advantage.
Impact on regular people
For SMB IT teams: For those with hard constraints against sending data to the cloud, ¥5,000-8,000 (~$700-1,100) now buys a usable local AI workstation — no longer a "tens of thousands to start" proposition.
For individual professionals: Programmers, researchers, and analysts can spend one weekend plus a consumer GPU to set up an offline AI assistant for sensitive documents.
For the consumer market: Getting AI into ordinary households requires more than "can run" — it needs to be "installable, stable, affordable." Today's 65 tokens/s is a technology inflection point, not a consumer one.