Can RTX 3060 12GB (32GB RAM) run Qwen3-VL 8B?
Measured results for Qwen3-VL 8B (8.77B dense) on RTX 3060 12GB (32GB RAM), plus what fits and the commands to run it.
Short answer
Yes. Qwen3-VL 8B Q4_K_M fits fully on RTX 3060 12GB (32GB RAM) at 16k context, using 7.4 of 11.0 GB usable, and generates about 61.5 tokens/s in our own run. As the context fills it slows down: 43 tok/s with 16k tokens already in context (70% of the empty-context speed), measured. For Q6_K, our bandwidth-only estimate was ~29 tok/s against a measured median of ~51 (42% too pessimistic).
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 61.5 | 61.5–61.5 | 1983 | 1 | includes our runs |
| Q8 class (median) | 39.3 | 39.3–39.3 | 2087 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 61.5 | 3 repeats | 1983 | 3 | own measurement |
| Q8_0 (our run, depth 0) | 39.3 | 3 repeats | 2087 | 3 | own measurement |
Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench b11658 on a rented GPU.
Speed vs context depth measured
Our own llama-bench runs on GeForce RTX 3060 12GB (flash attention on, 3 repeats): generation and prompt speed with the given number of tokens already in the context.
| Quant | Context depth | Generation tok/s | vs depth 0 | Prompt tok/s |
|---|---|---|---|---|
| Q4_K_M | 0 | 61.5 | 100% | 1983 |
| Q4_K_M | 4k | 55.4 | 90% | 1709 |
| Q4_K_M | 16k | 43 | 70% | 1183 |
| Q8_0 | 0 | 39.3 | 100% | 2087 |
| Q8_0 | 4k | 36.6 | 93% | 1776 |
| Q8_0 | 16k | 30.6 | 78% | 1215 |
Alternatives
Faster for Qwen3-VL 8B
- RTX 5070 12GB: Q6_K, 11 GB usable, ~94 tok/s scaled from measured
- RTX 4070 12GB: Q6_K, 11 GB usable, ~71 tok/s scaled from measured
- RTX 5080 16GB: Q8_0, 15 GB usable, ~99 tok/s measured
Other models on RTX 3060 12GB (32GB RAM), similar size
- Gemma 4 E4B (hardware page): Q8_0, fits fully, ~42 tok/s (scaled from measured)
- Qwen3.5 9B: Q8_0, fits fully, ~37 tok/s (measured)
- Ornith 1.5 9B (hardware page): Q8_0, fits fully, ~36 tok/s (scaled from measured)
- DeepSeek R1 Distill Qwen 7B (hardware page): Q8_0, fits fully, ~42 tok/s (scaled from measured)
Memory and context limits
Q6_K weights are 6.7 GB; the 16k f16 KV cache adds 2.42 GB. Largest context that fits in 11.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 16k |
| q8_0 | 32k |
| q4_0 | 64k |
Native limit 256k, 1M with YaRN. Full KV cache table for Qwen3-VL 8B.
Copy-paste commands
The advisor's pick for this hardware: Q6_K, 16k context.
llama-server -hf unsloth/Qwen3-VL-8B-Instruct-GGUF:Q6_K -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/Qwen3-VL-8B-Instruct-GGUF:Q6_K
FAQ
How fast is Qwen3-VL 8B on RTX 3060 12GB (32GB RAM)?
About 61.5 tokens/s for generation (Q4_K_M on llama.cpp CUDA, 128-token generation), 1983 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Qwen3-VL 8B need on RTX 3060 12GB (32GB RAM)?
The advisor's pick, Q6_K, is 6.7 GB of weights plus 2.42 GB of KV cache at 16k context (9.1 GB), against 11.0 GB usable. The measured Q4_K_M run needs 7.4 GB.
Is the Qwen3-VL 8B speed on RTX 3060 12GB (32GB RAM) measured or estimated?
Measured: our own llama-bench run on a rented GeForce RTX 3060 12GB, including runs with 4k to 16k tokens already in context.
What can I use instead of RTX 3060 12GB (32GB RAM) for Qwen3-VL 8B?
Nothing with less memory in our list fits it fully. Faster: RTX 5070 12GB (~94 tok/s), RTX 4070 12GB (~71 tok/s), RTX 5080 16GB (~99 tok/s).
What is the maximum context for Qwen3-VL 8B on RTX 3060 12GB (32GB RAM)?
With an f16 KV cache about 16k tokens, 32k with q8_0 and 64k with q4_0, bounded by the model's own limit.
Related
All models on RTX 3060 12GB (32GB RAM) · Qwen3-VL 8B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 3060 12GB (32GB RAM) vs 8GB VRAM class (RTX 4060 8GB, 32GB RAM)