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🔥 Score 57.9
workflow • Confidence 42%

TokenTuner: LLM Token & Latency Optimizer

Developers using large language models are hit with slow throughput—often only 10 tokens per second—and unreliable tool calls, especially when models emit tens of thousands of tokens before processing prompts. TokenTuner delivers a lightweight runtime that trims redundant output, guarantees correct prompt handling, and boosts token‑rate to 100+ tps while ensuring robust file‑write tool calls.

Quantitative Score Breakdown

complaint frequency
4.5
growth rate
20
competition density
10.5
monetization potential
9
technical feasibility
7.5
search interest
6.4

Evidence Signal (2)

Raw Posts
hn • r/hackernews

Comment on: Claude Code sends 33k tokens before reading the prompt; OpenCode sends 7k

I tried Qwen 3.6 27B, but I can only get around 10 tokens per second, but it's painfully slow, and it often fails during `write_file` tool calls, even with Qwen Code.
hn • r/hackernews

Comment on: 1-Bit LLM in the Browser

Qwen is quite slow in general.I'm getting ~0.5 tps from [qwen], and ~10 tps from [gemma] - roughly the same size, same quant, same hardware (8-core CPU) same software (llama.cpp).[qwen] Qwen3.6-27B-Q4_K_M.gguf[gemma] gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf