Unverified50% confidenceBenchmarkExact time
On the LLaDA and Dream models, evaluated on four benchmarks—GSM8K, HumanEval, MATH, and MBPP—dLLM-KVQ achieved the highest average score among all quantization methods in the reported 4-bit and 2-bit prefix/dual cache settings.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/30/2026
First Seen
Valid until: 11/13/2026
Sources
Related Entities
Related Claims
UnverifiedTaking the LLaMA-3 70B model as an example, with 80 layers, head_dim=128, and BF16 precision, each token occupies approximately 80KB of KV cache.68% similarUnverifiedWhen config.json is available, ModelFit uses the GQA formula to precisely calculate the KV cache: 2 × layers × kv_heads × head_dim × ctx × 2 bytes; otherwise it uses the heuristic rule of "+20% with a minimum of 1.5GB" and clearly marks this on the page.67% similarUnverifiedNorth-South Grid 4.3-33B has introduced GGUF-quantized full-weight support, with the base model supporting contexts up to 512K tokens and providing configuration examples for Llama.cpp and Ollama.66% similarUnverifiedLM Studio provides a graphical interface, is compatible with GGUF-format quantized models, and consumer-grade GPUs with 8GB of VRAM can run models of the 7B parameter scale smoothly.65% 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.65% similar
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