4 posts tagged “kv-cache”
At 32,000 tokens, the costliest thing my MacBook did was wait seven minutes to speak
I ran the same long-context test on a 16 GB fanless M3 and a ₹23 rented NVIDIA L4. The laptop fits a 32k context on an 8B model and keeps every planted fact — but prefill balloons to seven minutes and its decode speed can't even be measured, because the fanless chip throttles. A measured, cross-hardware look at the KV-cache tax.
Atomic Chat's TurboQuant headline did not survive a chat-generation benchmark on my M3
Atomic Chat advertises TurboQuant as 8x faster inference and 6x less memory. I tested the local MLX TurboQuant KV path on a 16 GB M3. It saved about 3-5% total peak memory and did not speed up generation — a useful reminder that KV-cache microbenchmarks do not automatically become whole-chat product claims.
I turned on MLX's memory-saving flag and ran out of memory
On a 16 GB Mac, MLX's --kv-bits flag — whose entire job is to shrink the KV cache so longer contexts fit — raised peak memory at every context length I tested, and OOM'd at 32K where plain fp16 fit at 9.4 GB. It's also no faster (8-bit decoding ran ~4× slower in my tests) and costs no quality you'd want to keep. Here's the measurement, the code-level cause, and why the flag backfires on this path.
Attention sinks: the four tokens that stabilize infinite context on a 16 GB Mac
StreamingLLM (2023) found that keeping four specific tokens in the KV cache prevents catastrophic perplexity collapse at long contexts. mlx-lm implements this as RotatingKVCache(keep=4). Here's what that means, why it works, and what our measurements on M3 actually show.