What LLM your machine can run — and which one is a good fit for a specific job.

All models

• DeepSeek R1-0528 Qwen3-8B R1's reasoning distilled into a Qwen3-8B body: frontier-class competition math in 8B — AIME'24 86.0 matches models 30× its size. A specialist: brilliant at math and logic, awkward at everything agentic.
• Gemma 3 12B vision The conversationalist with eyes. Natural prose, good multilingual chat, and built-in image understanding. Weaker than same-size rivals at code and tool calling — pick it for writing and vision, not agents.
• Gemma 3 27B vision The conversationalist at scale: best-in-family prose, strong multilingual chat, real vision — and the receipts show it is NOT a coding or agent model (Aider 4.9%).
• Gemma 3 4B vision The smallest model with real eyes: pleasant chat and image understanding on an 8GB laptop, with Google's QAT 4-bit build. Just don't hand it math or code.
• Gemma 4 12B vision The 'Unified' one: an encoder-free architecture that takes image and audio straight into the embedding space, at a size that leaves room to breathe on a 16GB machine. Punches near the 26B MoE on reasoning while being the only model here that hears.
• Gemma 4 26B-A4B vision Google's answer to the fast-MoE era: 26B of knowledge at 4B speed, vision, 256K window, QAT 4-bit weights, and finally an Apache license. The strongest all-rounder in the 16GB class right now.
• Gemma 4 31B vision The Gemma 4 family's dense flagship, and the best long-context model in this catalog by a wide margin — 66.4% MRCR at 128K where its own 26B sibling manages 44.1%. Official QAT Q4_0 is 17.7GB, so the whole thing fits a 32GB machine.
• Gemma 4 E2B vision The smallest Gemma 4, at 3.35GB in Google's QAT build — genuinely multimodal on hardware that cannot run anything else in this catalog. Use it for dictation, captioning and simple chat; its own vendor numbers rule out reasoning and tools.
• Gemma 4 E4B vision Phone-class Gemma: Per-Layer Embeddings mean 8B of weights behave like 4.5B at inference, and the official QAT build is 5.15GB. Text, image and audio in the 8GB tier — but the benchmarks are honest about the ceiling.
• GLM-4.7-Flash The agentic-coding MoE of the moment: SWE-bench 59.2 from 3B active params, MIT-licensed, ~200K context. Watch it at long context, where its tool calls start fraying.
• GLM-4.7 The full-size GLM behind the 30B Flash already listed here, and the strongest open coding scores on this site — SWE-bench Verified 73.8. It is also the most expensive model here to hold in memory: all 92 layers use full attention, so the KV cache costs 368KB per token, more than any other model in the catalogue.
• gpt-oss-120b The big sibling of gpt-oss-20b, and the same bargain at four times the size: shipped natively in MXFP4, so there is no quant ladder to choose from — one 63GB file is the model. Reasoning and tool use are excellent; world knowledge is not. It was built to look things up.
• gpt-oss-20b OpenAI's open MoE, shipped natively in MXFP4 (~4-bit) — the quant band question mostly answers itself. Fast, strong at STEM and tool use, and startlingly ignorant of the world: built to look things up, not to know them.
• Granite 4.2 30B IBM's enterprise reasoner with a thinking switch — one checkpoint that either reasons step by step or answers directly. Trained to use tools inside sandboxed environments rather than on transcripts of tool use, which shows in its function-calling scores. Five days old at time of writing; every number here is IBM's.
• Granite 4.2 8B The tool-calling specialist of the 8GB tier: Apache-2.0, a thinking switch, and function-calling trained in live sandboxes rather than scraped transcripts. Its predecessor's 8B matched a 32B MoE across ten benchmarks — this is that lineage, five days old.
• KAT-Coder-V2.5-Dev Qwen3.6-35B-A3B with a coding-specific post-train bolted on — same architecture, same footprint, tuned hard for agentic software work and reported to use far fewer tokens getting there. Vision was stripped from the open release. A useful demonstration of what post-training alone is worth.
• Llama 3.1 8B The compatibility baseline. Every local tool, tutorial, and fine-tune supports it, and its tool calling is still excellent — but 2025–26 peers beat it on raw reasoning. Pick it for ecosystem, not scores.
• Llama 3.2 3B Meta's edge-class model: built for summarization, rewriting, and on-device assistants, and honest about it. Runs on almost anything; don't ask it to be a scientist.
• Llama 3.3 70B The largest dense model in this catalogue — everything above it is a sparse MoE. Best instruction-following evidence on the site at IFEval 92.1, and it needs a 96GB machine to be comfortable at any real context, because its KV cache costs 10GB at 32K before the weights are counted.
• Mistral Small 4 vision Mistral folded Magistral, Pixtral and Devstral into one model: reasoning, vision and agentic coding, Apache licensed, 6.5B active of 119B. The catch is attention. It kept full multi-head attention where everything else its size uses grouped-query, so its KV cache costs 576KB per token — the most expensive on this site, and more than a 358B model's. Cheap to compute, dear to give context to.
• Muse Glimmer 30B vision Meta's agent-first comeback under a real Apache license: strong tool use, screen understanding, and a speculative-decoding sidecar for speed. Knowledge is its weak leg — it's built to act, not to recall.
• Nanbeige4.2 3B A looped transformer: 22 layers run twice, so 3B of parameters do the work of a much deeper model. Reported to beat Qwen3.5 9B and Gemma 4 12B on agentic coding at a third of the size. The loop is not free — it doubles the KV cache, which is the largest memory cost here by far.
• Nemotron 3 Nano 30B-A3B The long-context specialist of this catalog by a distance: 86.3% on RULER at one million tokens, where most models here are unrated and the best measured rival manages 66% at 128K. A hybrid Mamba-Transformer MoE — only 6 of its 52 layers hold a KV cache, which is why the window is affordable rather than nominal.
• Nemotron 3 Super 120B-A12B The Super tier of Nemotron 3, scaling the Nano's hybrid Mamba-MoE design to 120B. Only 8 of its 88 layers are attention, so the KV cache costs 8KB per token — a quarter of what a dense model this size would ask, and the reason a 256K context stays affordable on a machine that can hold the weights.
• Ornith-1.5 35B-A3B vision The highest SWE-bench Verified score in this catalog — 79.0, from 3B active parameters, under MIT. Trained by having the model invent its own tasks and scaffolds rather than learning from a fixed human-written set. Eleven days old, and every number is DeepReinforce's own.
• Ornith-1.5 9B 70.6 on SWE-bench Verified from a 9B dense model that fits a 16GB machine at 4-bit — a number that would have belonged to a 30B a year ago. Text only, MIT, and only its maker has measured it so far.
• Phi-4-mini Strong math and code for a 3.8B — and the worst measured hallucination rate on this site. A capable calculator that should never be trusted on facts without retrieval.
• Phi-4-reasoning-vision vision Microsoft's selective-reasoning vision model — it decides when to think. Catalogued for completeness with a hard local caveat: llama.cpp cannot run its vision tower, so local GGUFs are text-only; the headline capability needs the full weights via transformers.
• Phi-4 The exam ace with no life experience: superb STEM reasoning per GB, near-zero world knowledge, and only a 16K window. Excellent at closed problems you paste in; pair with retrieval for everything else.
• Qwen3 14B The sweet spot of the dense Qwen3 line: near-32B quality in a 16GB-friendly footprint, with the family's strong long-context showing (RULER 94.6 non-thinking).
• Qwen3 30B-A3B A mixture-of-experts model: 30B of knowledge, but only ~3B active per token, so it runs fast on machines that can hold it. The best quality-per-second story on 32GB Macs.
• Qwen3 32B The dense flagship under 35B — the only sub-35B model with an independent Aider coding score. Trades the 30B-A3B MoE's speed for steadier quality and more graceful quantization; expect <10 tok/s on unified memory.
• Qwen3 4B The strongest 4B all-rounder: with thinking mode on, it reasons like models twice its size. The default answer for 8GB laptops.
• Qwen3 8B The default mid-size all-rounder. Strong reasoning and coding for its size, hybrid thinking mode, solid tool calling. The community's most common answer to 'which model should I start with?' on 16GB machines.
• Qwen3-Coder-Next 80B-A3B Three billion active parameters out of eighty, and 70.6 on SWE-bench Verified — within striking distance of models that activate ten times as much. The 49GB 4-bit build is the cheapest way onto this site's upper tier, and the sparsity that makes it fast is also what makes it fragile at low quant.
• Qwen3.5 122B-A10B vision The Qwen3.5 that fits: 76GB at 4-bit puts it inside a 96GB machine with room for context, and it gives up surprisingly little to the 397B — 72.0 on SWE-bench against 76.4. The best accuracy-per-gigabyte on this site above 70B.
• Qwen3.5 397B-A17B vision The largest model on this site and the top of the Qwen3.5 line. Gated DeltaNet plus sparse MoE: only 15 of its 60 layers are full attention, so 256K of context costs about 8GB of cache — remarkably cheap for its size. The weights are the problem, not the cache.
• Qwen3.5 9B vision The most-downloaded local model of 2026 (12M+/month): vision, 256K context, and vendor claims that beat gpt-oss-20b from 9B dense. Sparse independent data so far — most cells honestly unrated.
• Qwen3.6 27B vision The 27B dense agentic-coding model that beat a 397B MoE on SWE-bench Verified — and, unlike its Qwen3.8 successor, has had four months for independent evaluators to check the claim. Q4_K_M is measurably indistinguishable from bf16 here; the real caveat is tool-calling reliability, not quantization.
• Qwen3.6 35B-A3B vision Near-flagship agentic coding at 3B-active speed: 73.4 on SWE-bench Verified while activating a twelfth of its weights. The trade is memory, not quality — you hold all 35B in RAM to run 3B worth of compute, and it carries the 3.6 family's tool-calling defect.
• Qwen3.8 27B vision The current Qwen open flagship under 35B: hybrid DeltaNet attention, vision, 256K context, and vendor benchmarks that look absurd — none independently replicated yet. Two weeks old; treat every number as a claim.