Expired50% confidenceBenchmarkExact time
In the actual test, the two models combined occupied only approximately 12GB of VRAM, with inference remaining stable below 12GB throughout.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/9/2026
First Seen
Valid until: 10/7/2026(expired)
Sources
DeepSeek DSpec Hands-On: How Speculative Decoding Accelerates LLM Inference by 6x
bilibili程序员-智能译站7/8/2026
Related Claims
Unverified8GB of VRAM can run 3B–7B quantized models, but inference speed is slow.70% similarUnverifiedUsing float16 precision as a baseline, each parameter occupies approximately 2 bytes, meaning a 7B model requires approximately 14GB of VRAM/memory, and a 70B model requires approximately 140GB.70% similarUnverifiedThe model weights alone of a 175-billion-parameter model (in BF16 precision) require 350 GB of storage, and the Adam optimizer must maintain two sets of states—first-order moments and second-order moments—totaling more than 1 TB.69% similarVerifiedStable Diffusion Checkpoint models are typically 2–7GB, while LoRA models are typically only tens to hundreds of MB.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/362543API
curl https://kongchang.com/api/v1/knowledge/claims/362543MCP
get_claim(id=362543)