Unverified50% confidenceBenchmarkExact time
A previous approach based on Shen Sanshu's publicly available technical documentation achieved decoding speeds approaching 400 tokens/s and prefill of approximately 2500 tokens/s in a local 16GB VRAM environment.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/10/2026
First Seen
Valid until: 1/8/2027
Sources
Related Entities
Related Claims
UnverifiedA 262K token context means the model can process approximately 200,000 Chinese characters in a single conversation, at the cost of the KV cache occupying large amounts of VRAM and memory as the context grows.73% similarVerifiedTaking 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.71% similarUnverifiedA 200K token context request requires approximately 25× the KV-Cache memory compared to an 8K token request.71% similarUnverifiedA log study measured the tokens each harness sends before reading the user's prompt: OpenCode approximately 7,000 tokens, CloudCode approximately 33,000 tokens, and Pi approximately 1,300 tokens.70% similarVerifiedA context window of 200K tokens can ingest approximately 150,000 lines of code at once, equivalent to the entire source code of a medium-sized commercial project.69% similar
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