Local AI Report #1 — The Mid-2026 Local LLM & Hardware Landscape
The first biweekly digest on the state of local LLMs: the mid-2026 model generation (Gemma 4, Qwen 3.6, DeepSeek V4), where GPU prices actually stand, and what the current best-value setup looks like.
- The mid-2026 open-model generation has settled: Gemma 4, Qwen 3.6 and DeepSeek V4 are the families to evaluate first if you last checked in early 2026.
- MoE architectures keep pushing capability per GB of VRAM down-market — the small active-parameter counts are what buy the efficiency.
- 24 GB cards remain the sweet-spot ceiling for single-GPU setups; used NVIDIA GeForce RTX 3090 pricing is the number to watch.
- Tooling churn is real: check your Ollama and LM Studio versions before pulling any of the new model families.
New & Notable Models
| Model | Params | VRAM (Q4) | Notes |
|---|---|---|---|
| Gemma 4 12B Unified | 12B | ~8 GB | Google’s June follow-up to the Gemma 4 line; the single-model answer for 16 GB machines. |
| Qwen 3.6 27B | 27B | ~16 GB | Efficiency refresh of the 3.5 dense line; the default general model for 24 GB cards. |
| Qwen 3.6 35B-A3B MoE | 35B total / 3B active | ~20 GB | MoE variant — fast token rates for its quality class thanks to the small active set. |
| DeepSeek V4-Flash | 284B total / 13B active | multi-GPU / 128 GB unified | The reasoning flagship you can actually self-host — if you have workstation-class memory. |
| Mistral Small 4 | 119B total / ~6.5B active | ~24 GB | Long-context (256K) MoE; borderline on 24 GB — check quant fit before committing. |
| IBM Granite 4.1 8B | 8B | ~6 GB | Hybrid Mamba2+Attention — the enterprise-friendly pick for 8 GB cards. |
Hardware Watch
The single-GPU picture hasn't changed structurally this fortnight: 24 GB of VRAM is still the ceiling that separates "runs everything sensible at Q4" from "picks its battles."
- New flagship tier: NVIDIA GeForce RTX 5090 street prices remain above launch MSRP. The premium over a used NVIDIA GeForce RTX 4090 still buys bandwidth more than capability for most local-LLM workloads.
- Value watch: used NVIDIA GeForce RTX 3090 remains the cheapest 24 GB entry. When its used-market price dips, it is the default recommendation again.
- 16 GB bracket: NVIDIA GeForce RTX 4060 Ti 16GB is still the budget pick for 14B-class dense models and the smaller MoEs.
- Apple side: Apple M4 Pro machines handle the new mid-size MoE families comfortably on unified memory; no price movement worth acting on this issue.
Tooling Updates
- Ollama — check which of the new families (Gemma 4 Unified, Qwen 3.6, Granite 4.1) ship official tags on your version. Hybrid-architecture models (Mamba2+Attention) have historically lagged a release behind.
- LM Studio — check MoE offload behavior on 16 GB cards before committing to the 35B-A3B class.
- llama.cpp — confirm whether Granite 4.1's hybrid blocks are upstream in your build; if not, the Ollama tag is the only practical path.
If you're on a setup from before June, the practical advice is unchanged: update the runtime first, then pull new models — not the other way around.
The Sweet Spot
- GPU: used NVIDIA GeForce RTX 3090 24 GB, or NVIDIA GeForce RTX 4060 Ti 16GB if buying new on a budget.
- Daily driver model: Qwen 3.6 27B at Q4 on 24 GB; Gemma 4 12B Unified on 16 GB.
- Reasoning fallback: DeepSeek V4-Flash via a cloud API for the rare tasks that exceed local capability — the hybrid pattern beats over-buying hardware.
Check your own card against these models with the GPU & VRAM checker — the fit verdicts there use the same VRAM math as our model pages.
Workshop Note
From recent workshop sessions: the most common mistake is still buying hardware before profiling the actual workload. Two attendees this month planned multi-GPU rigs for tasks a 27B dense model handles on a single 24 GB card. Start from the model that solves your problem, then buy the minimum hardware that runs it at Q4 with your typical context length — the cost calculator makes the break-even explicit.
Other issues: Report #6 — best local coding models by hardware tier