Best Local LLM by RAM (July 2026): 8GB to 128GB Picks

Pick Your RAM Tier (July 2026)

If you came from a Reddit-style model search, use the short versions first: Best local LLM Reddit picks for OpenClaw, Reddit’s favorite local LLM for OpenClaw, Best OpenClaw model Reddit users recommend, 32GB Reddit shortlist, 64GB Reddit shortlist, or 128GB Reddit shortlist. Those pages answer the “what does Reddit recommend?” phrasing directly, then route back here for the full RAM table.

What Changed by July 2026

The local LLM landscape moved fast between February and July 2026:

• Laguna XS 2.1 (Poolside) — 33B/3B MoE with a 256K context. It fits in about 36GB at Q8 and is now the best agentic-coding pick for the 48-64GB tiers. It is in the Ollama library.
• DeepSeek V4 Flash — Runs on Ollama cloud only (use the :cloud command). Local V4 support is still experimental forks, so treat it as cloud-first at every consumer RAM tier.
• Qwen 3.6 27B (April 22) — Dense 27B that outperforms the 397B Qwen 3.5 MoE on agentic coding (77.2 vs 76.x on SWE-Bench Verified). Still the default for 24-32GB tiers.
• GLM-5.1 (April 7) — 744B MoE from Z.ai. Cloud-only. (Earlier guides citing “GLM-5.1 32B” were referring to the older GLM-4 line, not 5.1.)
• Mistral Small 4 (March 16) — 119B-A6B MoE that fits at Q4 in about 60GB. Replaces Mistral Large 123B.
• Qwen 3.5 small series (March 2) — 0.8B / 2B / 4B / 9B variants. The 9B is the new 16GB tier pick.
• Qwen 3.5 medium (February 24) — 27B dense, 35B-A3B MoE, 122B-A10B MoE. The 35B-A3B MoE is excellent at 48GB.
• Llama 3.3 70B — Still works, no longer the default. The Qwen and gpt-oss families have caught up at smaller sizes.

How to Use This Guide

Step 1: Find your usable RAM, not your installed RAM. On Mac, the OS reserves 4-6GB. On Windows or Linux with an NVIDIA GPU, the relevant number is VRAM (the GPU’s onboard memory), not system RAM.

Step 2: Subtract context overhead. A 32K context window costs roughly 4-6GB. A 128K window costs 16-24GB. Model weights are not the only thing that has to fit.

Step 3: Pick the highest-quality quant that leaves headroom. Q5_K_M is the sweet spot. Q4_K_M is the standard. Below Q3 starts to hurt tool calling, which kills agent runs.

OpenClaw Tool-Calling Reality Check (July 2026)

Most local LLM guides talk about benchmark scores. For OpenClaw, only one metric matters: does the model emit valid JSON when asked to call a tool, hundreds of times in a row, without drift?

Models that pass this filter today:

• gpt-oss 20B — cleanest tool-call JSON in production, this is the safe default
• gpt-oss 120B — same family, scaled up
• Qwen 3.6 27B — fixed the tool-calling regressions from 3.5
• Qwen 3.6 35B-A3B (MoE) — fast inference with reliable tools
• Llama 3.3 70B — still fine for tool calls
• Mistral Small 4 (119B-A6B) — works, but heavier than gpt-oss

Models to avoid for OpenClaw right now:

• Qwen 3.5 27B — known broken tool-calling in Ollama (GitHub issue #14493)
• Anything under 7B — too unreliable for autonomous loops
• Most fine-tunes of base models

Quantization Cheat Sheet

🛒 Recommended hardware for local AI

The two Macs that handle the workloads on this page.

MINIMUM · 24 GB

Apple MacBook Pro M-series The 24 GB unified memory floor for seamless local AI. Runs 27B models at Q4 comfortably; perfect entry rig for OpenClaw + Ollama.

Check current price on Amazon → RECOMMENDED · 48 GB+

Premium Mac for 48 GB+ For 70B-class models, multi-model setups, and 8-hour autonomous OpenClaw loops without compromise.

Check current price on Amazon →

Amazon affiliate links — we earn a small commission at no cost to you.

Can Your RAM Run It? (exact-answer guides)

• Can I run Llama 3.3 70B with 64GB RAM? — the 70B-on-64GB question
• 128GB RAM + 24GB VRAM — offloading across RAM and a 24GB GPU
• 128GB RAM + 48GB VRAM — the workstation split
• 128GB RAM, no GPU — unified memory vs system RAM
• 64GB RAM + 24GB VRAM — the value workstation tier
• Can I run OpenClaw with 16GB RAM? — the tight-but-workable floor
• Can I run OpenClaw with 8GB RAM + 8GB VRAM? — the entry-level question

See Also

• Best Local LLM by GPU (hub) — the VRAM-tier matrix for discrete-GPU rigs
• How Much Context Fits in 128GB RAM? — memory headroom, KV cache, and context settings for the top RAM tier
• Best Local LLM Reddit Picks for OpenClaw — exact broad Reddit-intent hub before choosing a RAM tier
• Reddit’s Favorite Local LLM for OpenClaw — Reddit-intent hub for favorite-model and best-local-LLM searches
• Best OpenClaw Model Reddit Users Recommend — exact OpenClaw model query with workload routing
• 32GB vs 64GB RAM for Local LLMs — buying decision for the two mainstream serious tiers
• 64GB vs 128GB RAM for Local LLMs — buying decision for serious-work vs power-user machines
• Why Is My Local LLM So Slow? — diagnose RAM, VRAM, context, CPU fallback, and OpenClaw tool-loop latency
• Best Local Models for OpenClaw — model-first comparison
• OpenClaw Mac Mini Setup — turn a Mac mini into an always-on host

Apple Silicon chip guides:

• Best Local LLM for M4 Pro — the Mac mini / MacBook Pro M4 Pro tier (up to 64GB)

• Best Local LLM for Mac Studio M4 — M4 Max, up to 128GB, a quiet private LLM server

• Best Local LLM for Mac Studio M3 Ultra — up to 512GB for 70B at Q8 and 100B+ MoE

• Mac mini vs Mac Studio for Local LLMs — which Apple Silicon host to buy

• OpenClaw Costs Guide — when local pays back the hardware

• OpenClaw Troubleshooting — fixes for common local-model issues