[KongchangAI]
Unverified50% confidenceSolutionExact time

The deployment uses a llama.cpp server with 4-bit KV cache, context length set to 100,000, 32 layers offloaded to system memory, and lazy mode enabled to force the Ngram table to be read from SSD.

1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/18/2026
First Seen
Valid until: 12/17/2026

Sources

177B LLM on Budget GPUs? Running Qwen3 Flash Next Locally — Real-World Test

Related Entities

llama.cppproductQwen3 Flash Nextmodel

Related Claims

UnverifiedFor the LLaMA-2 70B model, when processing a context of 4096 tokens, the KV Cache requires approximately 20 GB of system memory at FP16 precision.73% similarUnverifiedDS4 retains an ultra-long context capability of 1M (1 million tokens) through KV cache disk persistence.70% similar

Cite This Claim

Stable URI
https://kongchang.com/claim/932903
API
curl https://kongchang.com/api/v1/knowledge/claims/932903
MCP
get_claim(id=932903)