InclusionAI’s team has published the technical report for the Ling-2.6 series, pushing Large Language Models to the trillion-parameter scale. Alongside the Ling-2.6-1T giant, a “flash” variant with 100 billion parameters aims for a balance between capability and inference cost. Yet in community chatter, attention is not solely on brute force: the extreme efficiency of smaller models is what captures the interest of on-premise operators.

A user recalled the performance of the earlier Ling-mini-2.0 family, a 16-billion-parameter Mixture-of-Experts model. With IQ4_XS quantization, it hit 160 tokens per second on a GPU with just 8 GB of VRAM. Even more surprisingly, a CPU-only setup with 32 GB RAM delivered between 50 and 70 t/s – a result the user labeled as unprecedented among other models, including those compressed to 1 bit.

When legacy matters more than size

There is no direct Ling-mini update for the 2.6 generation yet, but the community hopes InclusionAI will replicate the formula. The math is simple: if a 16B model reaches 160 t/s, a hypothetical 30B quantized at 4-bit could touch 80 t/s on the same graphics card. This projection, grounded in real-world experience, shows how software optimization and aggressive quantization can overturn constraints of consumer and enterprise hardware.

The meaning for on-premise adopters

For those evaluating self-hosted deployments, this is more than an academic figure. High tokens per second on modest hardware mean the ability to run LLMs locally without investing in multi-GPU systems or relying on cloud APIs. It opens scenarios of data sovereignty, reduced TCO, and predictable latency for agentic applications. Not all models, however, guarantee similar numbers: Ling-mini-2.0 remains an exception that demonstrates how efficiency-oriented design – from MoE architecture to quantization implementation – can make a real difference compared to the parameter race.

Outlook and unknowns

The Ling-2.6 report provides no details on future mini variants, but the existence of a 100B flash model suggests a focus on balance. Meanwhile, on-premise infrastructure managers can take away a lesson: it’s not just model size that determines usability, but the entire inference stack. The Ling series, with its history, reminds us that speed records on common hardware stem from design that puts efficiency first.