Local AI by VRAM Tier - 8GB | 12GB | 16GB | 24GB | 32GB
Deep dives: Chat | Coding | Translation | Vision | Speech | Agents
Updated July 2026: GPT-OSS 20B is still the 16GB chat pick, but the agent and speech choices moved on (Apriel-1.6-15B, Canary-Qwen, Step-Audio-EditX), and the honest read on 16GB vision is “a smaller model at higher precision,” not a bigger one.
16GB is the comfortable middle of local AI in 2026. The 15-20B models fit with headroom, small mixture-of-experts models become practical, and with the RTX 5060 landing at this tier it is one of the most popular VRAM classes for enthusiasts.
Your Hardware
16GB VRAM cards: RTX 4060 Ti 16GB ($450), RTX 5060 ($400, new), Intel Arc A770 ($250-300), AMD RX 7800 XT ($400). The Arc A770 is a budget standout - 16GB with decent compute, though its AI driver support is still maturing.
The math: at Q4_K_M, 16GB fits models up to about 20B parameters with 2-4GB for context. At Q5/Q6 (higher quality), 14-15B models fit with generous headroom. This is the tier where higher quantization becomes practical.
Quick Reference
| Use Case | Best Pick | VRAM Used | Note |
|---|---|---|---|
| Chat | GPT-OSS 20B | ~14 GB | Still the top 16GB pick |
| Coding (autocomplete) | Qwen2.5-Coder 14B | ~9 GB | ~89% HumanEval, FIM |
| Coding (agentic) | Qwen3.6-35B-A3B | ~16-22 GB | 73.4% SWE-bench (aggressive quant) |
| Translation | Purpose-built translation model | ~8 GB | Long-document consistency |
| Vision | Qwen3-VL-8B (Q6/Q8) + 2nd model | ~14 GB | No purpose-fit 16GB VLM exists |
| Speech (STT) | Canary-Qwen / Whisper large-v3 | 6-10 GB | ~5.63% WER (EN) / 99+ langs |
| Speech (TTS) | Step-Audio-EditX | ~12 GB | Top-ranked open TTS |
| Agents | Apriel-1.6-15B-Thinker | ~10-13 GB | MIT, strong tool use |
Chat & General Assistant
GPT-OSS 20B at ~14GB is still the top 16GB pick in the most recent tier-specific testing. It is fast and a capable all-rounder, and it now has real competition rather than a clear successor.
Reasoning alternative: Apriel-1.6-15B-Thinker - a 15B dense, MIT-licensed model with a high blended benchmark score and 131K context. Try it when GPT-OSS feels shallow on hard reasoning; 16GB gives it comfortable context room.
Full comparison: local chat model guide.
Coding
The dual-model setup shines here: run Qwen2.5-Coder 14B (~9GB) for fast FIM autocomplete with room for context on a 16GB card. For agentic work (a model that resolves issues across files), the current pick is Qwen3.6-35B-A3B at 73.4% SWE-bench Verified, but it is a tight fit here - an MoE keeps all experts resident, so expect to run it at an aggressive quant with modest context. If that is too tight, keep autocomplete local and reach to the 24GB tier for full agentic coding.
Full comparison: local coding model guide.
Translation
A purpose-built translation model in the ~8GB class (such as the larger TranslateGemma) leaves context headroom to load an entire document and translate it in one pass, keeping terminology consistent across long texts. Pair a small mechanical translator with a general LLM for context-aware literary work.
Full comparison: local translation model guide.
Vision
Here is the honest read on 16GB vision, unchanged from our deep dive: the open VLM families jump from ~6GB (8B-class) straight to ~18-21GB (30-32B-class), so there is no model built for the 16GB bracket. The best use of the VRAM is to run Qwen3-VL-8B at a higher-quality quant (Q6/Q8) for cleaner OCR, and keep a second model - a grounding model like Molmo2-8B or a small chat LLM - resident alongside it. That two-model workflow beats one starved big model.
Full comparison: local vision model guide.
Speech
16GB comfortably runs a full-quality speech pipeline. Transcription: Canary-Qwen-2.5B for the lowest English WER (~5.63%), or Whisper large-v3 (~10GB) when you need 99+ language coverage. Text-to-speech: Step-Audio-EditX (~12GB, or ~6-8GB as an AWQ build) is the top-ranked open TTS with zero-shot cloning and editable delivery; Chatterbox (~4-6GB) is the lighter cloning option.
Build a voice assistant by combining a light transcriber (Parakeet, ~2GB) + Chatterbox + a small chat LLM, all resident at once with room to spare.
Full comparison: local speech model guide.
Agents
Apriel-1.6-15B-Thinker is the 16GB agent pick: 15B dense, MIT-licensed, 131K context, with strong function-calling and TAU-bench numbers and reasoning training that helps it plan multi-step tool chains. GPT-OSS 20B remains a fast OpenAI-format alternative when you want one model for chat and tools.
Full comparison: local agent model guide.
Getting Started
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull essentials for 16GB
ollama pull gpt-oss:20b # Chat + coding + agents
ollama pull qwen2.5-coder:14b # Autocomplete
ollama pull qwen3-vl:8b # Vision (run at higher quant)
# Start chatting
ollama run gpt-oss:20bModel tags shift as libraries update - check the Ollama library for current ones.
The 16GB Advantage: Headroom
The difference from 12GB is not just bigger models, it is comfort. At 12GB, fitting a 14B model leaves 1-2GB for context; at 16GB you have 4-6GB free. That means longer conversations without truncation, bigger code files in prompts, higher quantization (Q5/Q6) for better quality, and room to run a small second model.
When to Upgrade
16GB handles most tasks well. The jump to 24GB unlocks:
- Qwen3.6-27B (dense) - the new 24GB default, and 77.2% SWE-bench Verified for coding
- GLM-4.7-Flash - the strongest agent model that fits a consumer card
- Qwen2.5-Coder 32B (92.7% HumanEval) - the best local autocomplete
- Qwen3-VL-32B - proper single-GPU document analysis
- Running multiple large models at once
See our 24GB guide for the enthusiast tier.