Models
76 open-weight models, sized
Updated 21 Aug 2026 5 new this week, 14 this month
Parameter counts, layer counts and attention shapes are read from each model's own config, because the KV-cache arithmetic depends on them exactly. Sizes below are at Q4_K_M and 8K context against a M3 Max · 64 GB. Missing a model that shipped this week? Open an issue — the table is hand-maintained.
| Model | Released | Params | Quant | Weights | Max context | Licence | On this rig | |
|---|---|---|---|---|---|---|---|---|
| Qwen3.5 4B The 8 GB coding agent. Q4 lands near 3.4 GB, leaving room for a real context window. |
28 Feb 2026 | 4.66B | Q4_K_M | 2.6 GB | 256K | Apache 2.0 | 85 tok/s | Check · Hardware |
| Qwen2.5-Coder 7B The standard local autocomplete model — small enough to keep resident all day, and still the best FIM model under 8B. |
Nov 2024 | 7.62B | Q4_K_M | 4.3 GB | 128K | Apache 2.0 | 52 tok/s | Check · Hardware |
| Ornith 1.5 9B New this week The small Ornith: a coding-agent reasoning build on the Qwen3.5 9B architecture. Same VRAM as its base, thinks before every answer. |
19 Aug 2026 | 9.41B | Q4_K_M | 5.3 GB | 256K | MIT | 42 tok/s | Check · Hardware |
| Qwen3.5 9B The default for 8–12 GB cards in 2026: beats every older 8B on every published benchmark, with vision. |
28 Feb 2026 | 9.65B | Q4_K_M | 5.4 GB | 256K | Apache 2.0 | 41 tok/s | Check · Hardware |
| Qwen2.5-Coder 14B Noticeably better at whole-file edits than the 7B, still comfortable on 12 GB. |
Nov 2024 | 14.8B | Q4_K_M | 8.3 GB | 128K | Apache 2.0 | 27 tok/s | Check · Hardware |
| Devstral Small 2 24B Built for software-engineering agents (OpenHands, Cline). Dense 24B, 384K window. Not in the Ollama library. |
Dec 2025 | 24B | Q4_K_M | 13.5 GB | 384K | Apache 2.0 | 17 tok/s | Check · Hardware |
| Qwen3.8 27B New this week The current default local Qwen: dense 27B, text + image + video, 262K context. Only 16 of its 64 blocks keep a KV cache, so long context is cheap. |
14 Aug 2026 | 27.8B | Q4_K_M | 15.6 GB | 256K | Apache 2.0 | 14 tok/s | Check · Hardware |
| Qwen3.6 27B The 24 GB coding pick of spring 2026 (77.2 SWE-bench Verified). Same shape as 3.8, one generation behind. |
22 Apr 2026 | 27.8B | Q4_K_M | 15.6 GB | 256K | Apache 2.0 | 14 tok/s | Check · Hardware |
| Muse Glimmer 30B New Meta's first open weights since Llama 4: a dense 30B distilled from Muse Spark for always-on local agents. Two KV heads keep the cache small. |
10 Aug 2026 | 29.8B | Q4_K_M | 16.8 GB | 128K | Apache 2.0 | 13 tok/s | Check · Hardware |
| Qwen3 Coder 30B-A3B Agentic coding MoE with a 256K native window. Still the most-downloaded local code model. |
Jul 2025 | 30.5B MoE | Q4_K_M | 17.1 GB | 256K | Apache 2.0 | 56 tok/s | Check · Hardware |
| GLM-4.7-Flash 30B-A3B MIT-licensed 30B-A3B tuned for agentic coding, with a DeepSeek-style latent KV cache. 60–80 tok/s reported on a 4090. |
20 Jan 2026 | 31.2B MoE | Q4_K_M | 17.5 GB | 198K | MIT | 62 tok/s | Check · Hardware |
| Qwen2.5-Coder 32B The first local code model that felt competitive with hosted assistants. Qwen3.8 27B is smaller and far ahead on agentic work. |
Nov 2024 | 32.8B | Q4_K_M | 18.4 GB | 128K | Apache 2.0 | 12 tok/s | Check · Hardware |
| Qwen3.6 35B-A3B Mixture of experts with ~3B active: the fastest serious model a 24 GB card runs, and the best MoE under 40B on agentic coding. |
16 Apr 2026 | 35.9B MoE | Q4_K_M | 20.2 GB | 256K | Apache 2.0 | 56 tok/s | Check · Hardware |
| Ornith 1.5 35B-A3B New this week A reasoning-first MIT build on the Qwen3.6 35B-A3B architecture (thinks before every answer). Same VRAM as its base. |
19 Aug 2026 | 35.9B MoE | Q4_K_M | 20.2 GB | 256K | MIT | 56 tok/s | Check · Hardware |
| Mistral Small 4 119B-A6B Instruct, reasoning, vision and code in one 119B MoE. A latent KV cache (320 wide) keeps context cheap; the weights still want 64 GB+. |
17 Mar 2026 | 119B MoE | Q4_K_M | 66.9 GB | 256K | Apache 2.0 | won't fit | Check · Hardware |
| Ling 3.0 Flash 124B-A5B New A 124B hybrid (5 linear-attention layers per MLA layer) with 5.1B active: SWE-bench Pro 56.6 and AIME 93 claimed. Built for 96–128 GB machines. |
2 Aug 2026 | 124B MoE | Q4_K_M | 69.7 GB | 256K | MIT | won't fit | Check · Hardware |
| Qwen3.5 122B-A10B The 96–128 GB unified-memory model: 122B of knowledge at 10B-active speed. |
24 Feb 2026 | 125B MoE | Q4_K_M | 70.3 GB | 256K | Apache 2.0 | won't fit | Check · Hardware |
| DeepSeek V4 Flash 284B-A13B New The V4 that 128 GB machines can actually run at Q3. Cache is modelled as a 576-wide latent; V4 compresses it further at long context, so this is conservative. |
31 Jul 2026 | 284B MoE | Q4_K_M | 159.7 GB | 1024K | MIT | won't fit | Check · Hardware |
| Ornith 1.5 397B-A17B New this week The flagship Ornith on the Qwen3.5 397B-A17B architecture, MIT-licensed. A 256 GB Mac Studio at Q4, and it is in the Ollama library. |
19 Aug 2026 | 397B MoE | Q4_K_M | 223.2 GB | 256K | MIT | won't fit | Check · Hardware |
| GLM-5.2 744B-A40B The strongest all-round open-weight model of mid-2026 on most public boards. Listed as a ceiling: 512 GB of unified memory at Q4. |
13 Jun 2026 | 753B MoE | Q4_K_M | 423.4 GB | 1024K | MIT | won't fit | Check · Hardware |
| Kimi K2.6 1T-A32B The open coding-agent benchmark leader of spring 2026 (80.2 SWE-bench). A 512 GB Mac Studio pair, or a ceiling. |
20 Apr 2026 | 1027B MoE | Q4_K_M | 577.5 GB | 256K | Modified MIT | won't fit | Check · Hardware |
| DeepSeek V4 Pro 1.6T-A49B New The 0813 refresh of the V4 flagship. Included as the honest ceiling; a terabyte of weights at Q4. |
13 Aug 2026 | 1650B MoE | Q4_K_M | 927.8 GB | 1024K | MIT | won't fit | Check · Hardware |
| Kimi K3 2.8T-A104B New The largest open-weight model ever published. 69 of 93 blocks are linear attention, so its cache is tiny; its 2.8T weights are the problem. |
27 Jul 2026 | 2780B MoE | Q4_K_M | 1563.2 GB | 1024K | Modified MIT | won't fit | Check · Hardware |
Weight sizes are computed from the parameter count and the quantisation's effective bits per weight, not read off a file listing — expect them to land within a few percent of the GGUF you actually download. The method, in full.