ATLAS is grabbing attention by running atop the Together Turbo Speculator stack, claiming up to 500 TPS on DeepSeek-V3.1 and 460 TPS on Kimi-K2, 2.65x faster than standard decoding and sometimes beating Groq [1]. But other data tell a messier story: Groq shows around 1,086 tps, while a Together setup supposedly hits 59 tps on the same task, raising questions about apples-to-apples comparisons [1]. OpenRouter chatter adds more noise, with hints that some numbers might reflect caching rather than pure decoding speed [1]. Moonshot benchmarks cited by the thread also remind us that the gap between top providers and the field isn’t trivial [1].
Hardware stacks in the wild - The debate isn’t just software tricks. Groq and Cerebras show up as the favorite exotic chips, but-throughput figures vary by model and baseline. OpenRouter data and other provider tests contrast with the Moonshot/OpenRouter pairing, underscoring the reality that hardware wins aren’t universal [1].
Memory and quantization constraints - Downshifting precision keeps giants usable on modest boxes. From Granite 4.0 Small Unsloth (32B, 4-bit) to Granite 4.0 Tiny Unsloth (7B, 4-bit) and Granite 4.0 Micro Unsloth (3B, 8-bit), quantization trims memory footprints [2]. Other options include Qwen 3 Instruct 2507 Unsloth (4B, 8-bit) and Qwen 3 Thinking 2507 Unsloth (4B, 8-bit), plus the large Qwen 3 Instruct 2507 Unsloth (30B, 4-bit) and OpenAI gpt-oss Unsloth (20B, 4-bit) [2]. The default Unsloth GGUFs path highlights a pragmatic trade-off: speed versus accuracy varies model-by-model, and memory bandwidth (e.g., iGPU vs CPU) often tilts the balance in favor of accelerator-backed setups [2].
Closing thought - In the real world, speed wins when you can tolerate some accuracy quirks; for bigger, riskier tasks, memory-friendly quantization and careful hardware choice keep the door open for practical, on-device usability. Watch how Tiny vs. Giant models, and 4-bit vs. 8-bit routes, play out in production. [1][2]
References
AdapTive-LeArning Speculator System (ATLAS): Faster LLM inference
Discusses LLM throughput, speculative decoding speedups, hardware comparisons (Groq, Cerebras), OpenRouter, and accuracy versus speed tradeoffs.
View sourceGPU Poor LLM Arena is BACK! 🎉🎊🥳
Arena back; new models; memory constraints; CPU vs GPU; MOEs; 4/8-bit quantization; model additions and discussions; community feedback performance sharing.
View source