idk if you want you can just read a few posts like this and get hyped for the next 2 months of the open source cambrian explosion 4-bit GPTQ Meaning: Model weights compressed to 4-bit using GPTQ quantization. Fit: Makes the 428B model physically fit across 2× DGX Sparks. Speed: Reduces memory bandwidth pressure, helping tok/s. Intelligence: Some quality loss possible, but much less than crude quantization. NVFP4 4-bit KV cache Meaning: Attention cache stored in NVIDIA 4-bit floating-point format. Fit: Critical for fitting 196K context; KV cache would otherwise be huge. Speed: Less memory movement per token, improving long-context generation. Intelligence: Main risk is degraded long-context recall/precision. KV cache Meaning: Stored attention history from previous tokens. Fit: Grows with context length; at 196K tokens it becomes a major memory consumer. Speed: Bigger cache slows attention unless compressed/optimized. Intelligence: Enables long-context reasoning, document recall, repo-scale prompts. EAGLE-3 Meaning: Speculative decoding method that drafts tokens ahead and verifies them. Fit: Not mainly a fit technology. Speed: Major boost to output tokens/sec. Intelligence: Usually preserves quality because the main model verifies outputs. Speculative decoding Meaning: A cheaper draft process guesses future tokens before the big model confirms. Fit: Small extra memory cost. Speed: Big win when many drafted tokens are accepted. Intelligence: Should not reduce intelligence if verification is exact. vLLM Meaning: High-performance inference engine/server for LLMs. Fit: Better memory management and KV-cache handling. Speed: Improves throughput, batching, and serving efficiency. Intelligence: No direct quality gain; just makes the model run better. Tensor parallel Meaning: Splits model computation across multiple GPUs/systems. Fit: Required to spread the model across 2× DGX Sparks. Speed: Helps compute scale, but interconnect overhead can hurt. Intelligence: No quality change if implemented correctly. MoE Meaning: Mixture of Experts; only some experts activate per token. Fit: Stores a huge model but only uses part of it per token. Speed: Much faster than running all 428B params every token. Intelligence: Keeps broad model capacity while reducing active compute. ~23B active params Meaning: Only ~23B parameters are used per generated token. Fit: Makes runtime compute feel closer to a 23B model, not dense 428B. Speed: Core reason 36 tok/s is plausible. Intelligence: Benefits from 428B total capacity, but each token uses a routed slice. 428B total params Meaning: Full stored size of the model across all experts. Fit: Impossible without heavy quantization/sharding. Speed: Huge memory footprint; needs MoE and optimized serving. Intelligence: Large total capacity supports broader knowledge and reasoning. 196K context Meaning: Can process up to ~196,000 tokens of prompt/history. Fit: Requires compressed KV cache. Speed: Long context normally slows generation. Intelligence: Enables massive document/codebase context, but precision may degrade at extreme lengths. No pruning / no REAP Meaning: They did not delete experts or shrink the model structurally. Fit: Harder to fit than a pruned model. Speed: Slower than pruning would be. Intelligence: Better chance of preserving full model capability.