§01·compatibility · /check
Qwen3-8B on RTX 4060 Ti 16GB
Yes — Qwen3-8B runs on the RTX 4060 Ti 16GB (16 GB). Fastest community-measured result: 45.8 tokens/s, with 16 GB peak VRAM.
✓ runsllmactive40 series16GB VRAM
§02·benchmarks
| Task | Quant | Speed | VRAM | Works | Confidence | Source | Verified |
|---|---|---|---|---|---|---|---|
| llm | Q4_K | 45.8tokens/s | 16GB | ✓ | hardware-corner.net· web | 2026-05-15 |
§03·common questions
Can you run Qwen3-8B on RTX 4060 Ti 16GB?
Yes — Qwen3-8B runs on the RTX 4060 Ti 16GB (16 GB). Fastest community-measured result: 45.8 tokens/s, with 16 GB peak VRAM.
How much VRAM does Qwen3-8B need on RTX 4060 Ti 16GB?
Measured peak VRAM is 16 GB.
Which quantizations have been tested for Qwen3-8B on RTX 4060 Ti 16GB?
Q4_K — measured in community benchmarks.
How fast is Qwen3-8B on RTX 4060 Ti 16GB?
Up to 45.8 tokens/s (llm), the fastest community-measured result.
Are there step-by-step instructions for Qwen3-8B on RTX 4060 Ti 16GB?
Yes — a step-by-step recipe documents Qwen3-8B on the RTX 4060 Ti 16GB; see the recipes listed below.
§04·related recipes
- llmbeginner16GB+
Qwen3-8B on RTX 4060 Ti 16GB: Q4_K_M GGUF via Ollama or llama.cpp
- llmbeginner5GB+
Qwen3-8B on Apple M2 Pro: local 8B chat with MLX 4-bit in 16 GB unified memory
- llmbeginner9GB+
Qwen3-8B on RX 7800 XT: 16 GB ROCm via Ollama or llama.cpp-HIP GGUF
- llmbeginner6GB+
Qwen3-8B on RX 7900 XTX: ROCm via Ollama or llama.cpp-HIP
- llmbeginner12GB+
Qwen3-8B on RTX 3080 Ti 12GB: Q4_K_M GGUF via Ollama or llama.cpp