Victor Grösser

Open-Weight Models, April–October 2026: Gemma 4, DeepSeek V4, Kimi K3 and the Shift East

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Period covered: April–October 2026. Written on 20 October 2026.

Open weights matter to me for a simple reason: they let you run AI on your own hardware, under your own rules. For education and legal work, where data often must not leave a server you control, that is not a nice-to-have. This post looks at the open-weight releases of the past six months, what you can realistically run, and the licences attached. The closed-model side is in my April–June and July–October recaps.

April: Gemma 4 goes Apache 2.0

Google announced Gemma 4 on 2 April 2026 (the developer release notes list the weights on 31 March) in four sizes: Effective 2B (E2B), Effective 4B (E4B), a 26B mixture-of-experts and a 31B dense model. The bigger news for me was the licence: Google's open-source blog called Gemma 4 the first Gemma models released under the OSI-approved Apache 2.0 licence, instead of the custom Gemma terms.

Some practical numbers from Google's own pages:

  • The 26B MoE activates only about 3.8B parameters per token, which is what makes it fast.
  • Unquantised bfloat16 weights of the 26B and 31B fit on a single 80 GB H100.
  • Google's memory table puts the 31B at about 17.5 GB at 4-bit and the 26B A4B at about 14.4 GB, which is consumer-GPU territory.
  • E2B text-only on mobile is listed at about 0.84 GB.
  • At launch Google said the 31B ranked #3 among open models on the Arena AI text leaderboard and the 26B #6.

The family kept growing: multi-token-prediction variants on 16 April, quantisation-aware (QAT) checkpoints in early June, and a Gemma 4 12B Unified model on 3 June that replaces separate vision and audio encoders with direct linear projections.

April: DeepSeek V4 preview

On 24 April DeepSeek released the V4 preview with open weights and a one-million-token context. According to The AI Cook's timeline, V4-Pro has 1.6 trillion parameters (49B active) and V4-Flash 284B (13B active). Impressive, but not homelab material: even with few active parameters, you still need memory for all the weights.

July–August: very large and very small

  • Kimi K3 (Moonshot AI, late July) was reported as the largest open-weight model so far at 2.8 trillion parameters, with only 16 of its 896 experts active per token. It briefly topped a coding arena.
  • DeepSeek V4 Flash 0731 (3 August) shipped under the MIT licence.
  • Meta Muse Glimmer (11 August): Meta open-sourced a 30B agentic model that runs on a single GPU, a few weeks after it started charging for its frontier Muse Spark models.
  • Qwen3.8-27B (Alibaba, 17 August): a dense multimodal model under Apache 2.0 that can run locally and be used commercially.
  • GLM-5.3-Flash (Z.ai, 27 August), MIT-licensed, alongside a Qwen3.8-Flash-Next preview of a new architecture.

September: DeepSeek V4.1-Flash and MiMo

DeepSeek's V4.1-Flash (10 September) is the release I found most technically interesting. DeepSeek describes it as a 552B-parameter MoE with a new "causal encoder–decoder" design: 8B active parameters for input and 16B for output. It claims the KV cache needs a quarter of the HBM and an eighth of the SSD storage of the previous generation, and that it beats its own V4-Pro, which it started routing to V4.1-Flash on 14 September. The weights are on Hugging Face, and DeepSeek says it will work with the open-source community on inference support.

The KV-cache point matters more than the parameter count for anyone running agents: long agent sessions are dominated by context, and cache-hit costs are, in DeepSeek's words, often a large share of agent costs.

Later in the month Xiaomi's MiMo-V2.6 Pro (22 September), an MIT-licensed 1.02-trillion-parameter model, claimed the top open-weight spot according to The AI Cook.

October: Europe shows up

On 5 October Aleph Alpha released Kolibri, an Apache 2.0 German-English model of 78B parameters with about 3.5B active, explicitly aimed at data that has to stay on your own servers. It's the kind of model I'd like to see more of from Europe, especially given the transparency duties under the EU AI Act.

The counter-trend

Meta's paid Muse Spark 1.1 in July broke with its open-weights history, even if Muse Glimmer later came out open. The AI Cook summarises the year as "open weights went east": almost every leading open release came from Chinese labs (DeepSeek, Moonshot, Alibaba, Xiaomi, Z.ai). Open is not a given, and it makes the labs that keep publishing weights, under clear licences, more valuable.

What I'd actually run at home

Being honest about hardware: a trillion-parameter MoE is "open" but not "local" for most of us. For a single consumer GPU or a small homelab, the realistic candidates from this period are:

  • Gemma 4 26B A4B or 31B at 4-bit (roughly 14–18 GB per Google's table) for general reasoning and agent experiments.
  • Qwen3.8-27B where a dense, Apache-licensed multimodal model fits better.
  • Muse Glimmer 30B for agentic tasks on one GPU, licence permitting.
  • Gemma 4 E2B/E4B for on-device and edge experiments.

My takeaway: mixture-of-experts with few active parameters, plus good quantisation, makes local, privacy-friendly AI realistic for education and legal tooling. Read every licence, though: Apache 2.0 and MIT are easy; custom licences are not. For the tools I use to serve these models, see links & repos and the Homelab & Infra category.

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