Articles tagged "Open Weight LLM"

DeepSeek V4-Flash needs roughly 160GB at 4-bit and V4-Pro about 920GB. Quick-lookup VRAM, RAM and GPU tables by quantization for both MIT-licensed open-weight MoE models, plus official API pricing and the deepseek-chat / deepseek-reasoner retirement on July 24, 2026. Updated July 2026.

Nemotron 3 Nano runs in about 18GB at 4-bit, Super needs 8x H100-80GB, Ultra needs 4x B200 at NVFP4. VRAM, GPU and quantization tables for NVIDIA's open-weight MoE family. Updated July 2026.

GLM-5.2 needs roughly 430GB at 4-bit and about 1.5TB at BF16 in combined memory. Quick-lookup VRAM, RAM and quantization tables for running Z.ai's 753B / ~40B-active open-weight MoE (MIT-licensed) locally. Updated July 2026.

Inkling needs from ~280GB (1-bit) to ~600GB (4-bit) combined memory, and 1.9TB at BF16. Quick-lookup VRAM, RAM, disk and quantization tables for running this 975B / 41B-active open-weight MoE locally. Updated July 2026.

Kimi K3 is a 2.8-trillion-parameter open-weight MoE (weights due July 27, 2026), ranked #1 on the frontend-code arena. API pricing is $3 input / $15 output per million tokens. Local runs are estimated at 650GB–1TB, needing server-class hardware. How it differs from K2.

A primary-source deep dive on **Kimi K2.7-Code**, released June 12, 2026 by Moonshot AI (Beijing). Grounded in the [Hugging Face model card](https://huggingface.co/moonshotai/Kimi-K2.7-Code), [MarkTechPost](https://www.marktechpost.com/2026/06/12/moonshot-ai-releases-kimi-k2-7-code-a-coding-model-reporting-21-8-on-kimi-code-bench-v2-over-k2-6/), and [VentureBeat's skepticism piece](https://venturebeat.com/technology/kimi-k2-7-code-cuts-thinking-tokens-30-practitioners-say-benchmarks-dont-check-out). Covers the 1T-total / 32B-active MoE architecture (384 experts, 8 routed + 1 shared), 256K context, MoonViT ~400M vision encoder, native INT4, forced-on thinking mode. License is **Modified MIT** (attribution required only above 100M MAU or $20M MRR), API pricing is $0.95 input / $0.19 cache-hit / $4.00 output per 1M tokens — roughly **1/18 of Claude Opus 4.8's output price**. OpenAI + Anthropic-compatible endpoints drop straight into Claude Code / Cursor / Aider / Cline / cmux. Moonshot self-reports **+21.8% vs K2.6 on its own Kimi Code Bench v2 and -30% reasoning tokens**, but **all public benchmarks are Moonshot's own proprietary suites; independent SWE-bench Verified / Pro / FrontierCode scores are not yet available as of June 15, 2026** (VentureBeat). For Japanese enterprises the column flags two critical caveats: **(1) both `api.moonshot.cn` and the Singapore-subsidiary-run `api.moonshot.ai` remain exposed to PRC National Intelligence Law Article 7 compelled disclosure (set against Japan's PPC DeepSeek alert of February 3, 2025 and the Digital Agency notice of February 6, 2025), and (2) the only reliable mitigation is Hugging Face self-hosting (~4-8 H100, ~595GB INT4) following the Mizuho / Lion Qwen-on-domestic-infrastructure precedent**.