Articles tagged "VRAM"

LTX-2.5 (22B DiT, Gemma 4 encoder) is an open video+audio model out Aug 11, 2026. VRAM: 16-80GB; files: 35.9-71.4GB by quant. See table and GPU picks.

Qwen3.8-27B weights landed on August 15, 2026. This at-a-glance requirements guide maps VRAM needs to the actual published file sizes: Q4_K_M is 17.1GB and won't fit a 16GB GPU, making IQ4_XS (15.7GB) the practical floor. Licensed Apache-2.0 for commercial use.

Comparing local LLM engines: Ollama and llama.cpp solo, MLX on Apple Silicon, vLLM for concurrent serving, TensorRT-LLM for NVIDIA, LM Studio for GUI trials.

Liquid AI's LFM2.5-2.6B is a 2.69B on-device agent model under 2.5GB with 128K context. This guide covers VRAM sizing, benchmarks, throughput, and licensing.

Shieldstral 1.0 3B is Mistral AI's Apache 2.0 moderation model, released Aug 4, 2026. It needs ~16GB VRAM in BF16 and screens both text and images together.

MiniMax H3, an open video+audio model, released weights Aug 3, 2026. ComfyUI needs ~42.5GB files, ~24GB VRAM (12GB may work). Covers quantization, GPU sizing.

WASTE is a dependency-free C engine running Kimi K3 (2.78T params) on 29GB RAM by streaming MoE experts from NVMe. Covers real throughput, setup, and limits.

LG AI Research's K-EXAONE 2.0 750B-A37B needs ~1.5TB weights at BF16, ~750GB at FP8, and 375-420GB at 4-bit — hardware math and Apache 2.0 self-host limits.

Moonshot released Kimi K3 open weights on July 26, 2026. At MXFP4 the weights alone are ~1.4TB — self-hosting means a multi-node cluster, not one GPU.

Start with Q4_K_M; step up to Q5_K_M or Q6_K if you have VRAM headroom. This guide explains GGUF naming, the quality/speed/VRAM tradeoffs per level, IQ (imatrix) quants, and how to choose by task. Updated July 2026.

LongCat-2.0 is Meituan's 1.6T-parameter, MIT-licensed MoE model. Running it locally needs roughly 3,800GB in BF16 or about 970GB even at INT4 — no personal PC can hold it. Requirements tables and API pricing ($0.75/$2.95 per 1M tokens), updated July 2026.

DeepSeek V4-Flash needs roughly 160GB at 4-bit and V4-Pro about 920GB. The July 31 V4-Flash-0731 build keeps the same footprint while beating V4-Pro on agent benchmarks. VRAM, RAM and GPU tables by quantization, API pricing, and the legacy model retirement. Updated August 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.

Gemma 4 needs 5GB VRAM (E2B/E4B), 16GB (26B MoE), or 24-62GB (31B Dense) depending on quantization. Requirements by model: RTX 3060 to H100, Apple Silicon M1-M4, CPU-only operation, RAM sizing, and budget builds. Updated July 2026.

Gemma 4 minimum: 5GB RAM (E2B Q4), recommended: 24GB VRAM (31B Dense Q4). Quick-lookup tables covering VRAM, RAM, and GPU requirements for all variants: E2B, E4B, 26B MoE, and 31B Dense.