Not sure which model to run? This guide cuts through the noise. We recommend the single best model per VRAM tier — the one most users should start with.
32 GB+ VRAM (RTX 5090, Apple M4 Max, Professional GPUs)
The Quick Reference Table
Not sure which model to run? This guide cuts through the noise. We recommend the single best model per VRAM tier — the one most users should start with.
4 GB VRAM (Budget GPUs / iGPUs)
Best pick: Phi-4 Mini (3.8B, Q4_K_M)
VRAM needed: 3.2 GB
Speed: ~35–50 t/s on RTX 4060, ~20 t/s on integrated graphics
Ollama: ollama run phi4-mini
Why: Microsoft's Phi-4 Mini outperforms models twice its size on reasoning and coding tasks. Fits in 3.2 GB, leaving headroom for context.
Runner-up: Gemma 3 4B (ollama run gemma3:4b) — better for creative writing, 3.5 GB.
What to expect: Capable assistant for everyday chat, coding help, and writing. Don't expect 70B-level reasoning, but for 4 GB, this is remarkable.
8 GB VRAM (RTX 4060, RX 7600, laptop GPUs)
Best pick: Llama 3.1 8B (Q4_K_M)
VRAM needed: 5.8 GB
Speed: 55–90 t/s on RTX 4060 / 4070
Ollama: ollama run llama3.1:8b
Why: Meta's Llama 3.1 8B is the most widely tested 8B model. Strong at all tasks, extensive community support, fast on 8 GB cards.
Runner-up for coding: Qwen 2.5 Coder 7B (ollama run qwen2.5-coder:7b) — best pure-coding 8B model.
Runner-up for reasoning: DeepSeek R1 8B Distill (ollama run deepseek-r1:8b) — near-GPT-4-level reasoning in 8B.
12 GB VRAM (RTX 4070, RTX 3060 12GB, RX 7700 XT)
Best pick: Qwen 3 8B (Q8_0) — run at higher quality since you have headroom
VRAM needed: 9.5 GB
Speed: 60–95 t/s
Ollama: ollama run qwen3:8b
Why: With 12 GB VRAM, you can run 8B models at Q8_0 (near-lossless) quality instead of Q4, meaningfully improving outputs.
Or try: Gemma 3 12B Q4 (ollama run gemma3:12b) — Google's best mid-size model.
Why: 14B models hit a quality sweet spot. Qwen 3 14B is state-of-the-art at this size — beats GPT-3.5 on most tasks.
Runner-up for coding: Qwen 2.5 Coder 14B (ollama run qwen2.5-coder:14b)
Best for long documents: Mistral Small 3.1 (ollama run mistral-small) — 128K context window.
24 GB VRAM (RTX 4090, RTX 3090, RX 7900 XTX)
Best pick: DeepSeek R1 32B (Q4_K_M)
VRAM needed: 19.5 GB
Speed: 45–55 t/s
Ollama: ollama run deepseek-r1:32b
Why: DeepSeek R1 32B is a reasoning powerhouse that fits in 24 GB and rivals GPT-4o on math, coding, and logic. A game-changer for 24 GB GPU owners.
Alternative: Qwen 3 32B (ollama run qwen3:32b) — also fits, stronger at general tasks.
32 GB+ VRAM (RTX 5090, Apple M4 Max, Professional GPUs)
Best pick: Llama 4 Scout (Q4_K_M)
VRAM needed: 12–14 GB (leaves 18+ GB for a massive context window)
Speed: 175 t/s on RTX 5090
Ollama: ollama run llama4:scout
Why: With 32 GB VRAM, you can run Llama 4 Scout (17B active parameters, 10M context) at blazing speed with enormous context — useful for long documents, codebases, or multi-turn conversations.
For 70B models: Llama 3.3 70B Q4_K_M needs 42 GB — works on Apple M2/M4 Ultra (128–192 GB unified memory) or dual RTX 4090/5090 setups.