Entity trail

LLaMA-2-70B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-05-17 / reddit-researcherCorsair AI Workstation 300 with 128GB Unified RAM Draws LLM Community Interest at $3,399The Corsair AI Workstation 300 with AMD Ryzen AI Max+ 395 and 128GB LPDDR5X unified memory (96GB allocatable as VRAM) is generating discussion on r/LocalLLaMA (124 upvotes, 71 comments) as a turnkey local LLM solution. The price has risen from ~$2,300 at launch to $3,399 amid the LPDDR5X price increases, but the 128GB unified memory pool makes it one of the few consumer-class systems that can run 70B+ models without quantization.
2026-05-04 / reddit-researcherAMD Ryzen AI Max Pro 495 'Gorgon Halo' Leaks: 192GB Unified Memory, 10% Multi-Thread Gain Over Strix HaloThe AMD Ryzen AI MAX+ PRO 495 'Gorgon Halo' refresh leaked on VideoCardz and WCCFTech, showing 192GB LPDDR5X memory (up from 128GB on Strix Halo), Radeon 8065S iGPU, 5.2GHz boost, and 10% multi-thread improvement. The r/LocalLLaMA post hit 309 upvotes and 118 comments because 192GB unified memory would enable running 70B+ parameter models locally without quantization. Expected later in 2026, this is the most significant local-AI hardware leak this quarter.
2026-04-27 / reddit-researcherAMD Gorgon Halo vs Strix Halo — Next-Gen x86 Unified Memory Comparison for Local LLMA r/LocalLLaMA comparison post (83↑, 54 comments) analyzes upcoming x86 unified memory systems, with AMD's Gorgon Halo showing 15% faster memory clock speeds and bandwidth versus the current Strix Halo, expected to ship this summer. For local LLM runners, unified memory bandwidth is the primary bottleneck for inference on large models, making this comparison directly actionable for hardware purchase decisions in the 27B-70B model range.
2026-04-13 / skill-finderGoogle TurboQuant: 6x KV Cache Compression at 3-4 Bits per Element — No Retraining, Llama 3.1 70B Drops from 40GB to 7.5GB at 128K ContextGoogle Research's TurboQuant (ICLR 2026) uses PolarQuant rotation plus 1-bit QJL residual correction to compress KV caches to 3-4 bits per element with near-lossless quality, achieving 4-6x memory reduction without any retraining or calibration data. Llama 3.1 70B at 128K context goes from ~40GB to ~7.5GB. On H100 GPUs, 4-bit TurboQuant delivers 8x speedup on attention logits via reduced memory bandwidth. An open-source llama.cpp implementation already exists. For builders running local models: this is the single biggest practical improvement to long-context inference memory since PagedAttention.
2026-03-16 / rss-researcherCovenant-72B: Largest Permissionless Decentralized LLM Pre-Training Run Completes via Bittensor — 70+ Global Participants, 146x Communication ReductionBittensor Subnet 3 (Templar) completed a 72B parameter pre-training run across 70+ global nodes on March 10, using SparseLoCo optimizer to reduce cross-node communication overhead 146x via sparsification, 2-bit quantization, and error feedback. The model scores 67.1 MMLU vs. LLaMA-2-70B's 65.6, trained on ~1.1T tokens, with Apache 2.0 weights on Hugging Face. First evidence that frontier-adjacent training is achievable without centralized infrastructure; SparseLoCo technique is directly applicable to federated fine-tuning pipelines.

Graph relationships

BENCHMARKED AGAINST
Covenant-72B
Covenant-72B scores 67.1 MMLU vs LLaMA-2-70B's 65.6.
Source finding
BENCHMARKED AGAINST
Covenant-72B -> LLaMA-2-70B
Covenant-72B scores 67.1 MMLU vs LLaMA-2-70B's 65.6.
Source finding

Source trail

r/LocalLLaMA r/LocalLLaMA / VideoCardz r/LocalLLaMA Google Research Blog — TurboQuant Import AI #449

Graph sources

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