Expired60% confidenceBenchmarkExact time
Taking Llama-3 70B as an example, the prefill stage for processing 32K tokens on an A100 80GB GPU takes approximately 800–1200ms, while processing 2K tokens takes only about 50ms.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/6/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/4/2026(expired)
Sources
In-Memory Layer Mapping: How to Effectively Solve LLM Context Overload
hackernewshackernews7/4/2026
Related Claims
VerifiedTaking LLaMA-2 70B as an example, when processing a 4096-token context with a batch size of 32, the KV Cache can consume over 80 GB of memory.78% similarUnverifiedUnder the M5 Ultra's 1.2 TB/s bandwidth, the theoretical speed of a 100 GB quantized model is approximately 12 tokens/s, a 200 GB model approximately 6 tokens/s, and a 300 GB model approximately 4 tokens/s.78% similarUnverifiedOn an A100 (80 GB VRAM) GPU, the throughput for training a 7B model is approximately 3,000–5,000 tokens per second.77% similarVerifiedTaking the LLaMA-2 70B model as an example, a sequence of 2048 tokens requires approximately 4 GB of VRAM for the KV cache alone.76% similarUnverifiedThe GPU multi-level memory hierarchy from highest to lowest is: register file (latency ~1 cycle), shared memory/L1 cache (~32–228 KB, latency ~20–40 cycles), L2 cache (H100 ~50 MB, latency ~200 cycles), and HBM global memory (latency ~500–800 cycles).75% similar
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