NVIDIA · 8 GB
The mainstream 4B–8B tier. Some 12B INT4 builds fit tightly with modest context.
Qwen3 8B
The highest listed result may be a tight or offloaded fit. For daily use, prefer the first result marked С запасом and keep context modest.
Ollama
One-command local chat and app integration. GPU and driver support varies by operating system. Confirm the current hardware matrix before buying hardware. Since v0.34.2 (2026-09-15) Ollama's first-run setup offered account sign-in, and the release notes said you could continue locally; since v0.40.1 (2026-10-07) the CLI Welcome screen completes onboarding on Enter and the intermediate account step is gone, while cloud models keep the sign-in flow. Since v0.40.0 (2026-09-25) Ollama runs MLX-supported model architectures on Apple silicon through MLX by default rather than its own runner; NVIDIA and CPU paths are unchanged.
Открыть гидПоказательные модели с открытыми весами
Qwen3 8B
- Оценка памяти
- ~6.8 GB
- Контекст
- 32K+
Qwen3 4B
- Оценка памяти
- ~3.6 GB
- Контекст
- 32K+
Gemma 3 4B
- Оценка памяти
- ~4.2 GB
- Контекст
- 128K
Llama 3.2 3B
- Оценка памяти
- ~2.8 GB
- Контекст
- 128K
Gemma 3 1B
- Оценка памяти
- ~1.4 GB
- Контекст
- 32K
Fit is based on estimated total model memory. Driver support, KV cache, multimodal projectors, concurrency, and desktop applications can all reduce available headroom. Current fit: Qwen3 8B (Впритык), Qwen3 4B (С запасом), Gemma 3 4B (С запасом), Llama 3.2 3B (С запасом), Gemma 3 1B (С запасом).