Can 8GB VRAM class (RTX 4060 8GB, 32GB RAM) run Qwen3-VL 8B?
Measured results for Qwen3-VL 8B (8.77B dense) on 8GB VRAM class (RTX 4060 8GB, 32GB RAM), plus what fits and the commands to run it.
Short answer
It runs on 8GB VRAM class (RTX 4060 8GB, 32GB RAM) only with partial offload (slow): 7.4 GB needed against 7.0 GB usable, and the measured speed is about 51.3 tokens/s. As the context fills it slows down: 34.4 tok/s with 16k tokens already in context (67% of the empty-context speed), measured.
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 51.3 | 51.3–51.3 | 2445 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 51.3 | 3 repeats | 2445 | 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 4060 (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 | 51.3 | 100% | 2445 |
| Q4_K_M | 4k | 45.6 | 89% | 2094 |
| Q4_K_M | 16k | 34.4 | 67% | 1376 |
Alternatives
Qwen3-VL 8B does not fit fully here. It does fit fully on:
- Mac mini / iMac M4 16GB: Q4_K_M, 11 GB usable, ~16 tok/s scaled from measured
- RTX 3060 12GB (32GB RAM): Q6_K, 11 GB usable, ~51 tok/s scaled from measured
- 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
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 8GB VRAM class (RTX 4060 8GB, 32GB RAM), similar size
- Gemma 4 E4B (hardware page): Q5_K_M, fits fully, ~47 tok/s (scaled from measured)
- Qwen3.5 9B: Q4_K_M, fits fully, ~46 tok/s (measured)
- Ornith 1.5 9B (hardware page): Q4_K_M, fits fully, ~44 tok/s (scaled from measured)
- DeepSeek R1 Distill Qwen 7B (hardware page): Q5_K_M, fits fully, ~48 tok/s (scaled from measured)
Memory and context limits
Q4_K_M weights are 5.0 GB; the 16k f16 KV cache adds 2.42 GB. Largest context that fits in 7.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | - |
| q8_0 | - |
| q4_0 | - |
Native limit 256k, 1M with YaRN. Full KV cache table for Qwen3-VL 8B.
Copy-paste commands
The advisor's pick for this hardware: Q4_K_M, 16k context.
llama-server -hf unsloth/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M -c 16384 -fa on --jinja -ngl 33
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M
FAQ
How fast is Qwen3-VL 8B on 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
About 51.3 tokens/s for generation (Q4_K_M on llama.cpp CUDA, 128-token generation), 2445 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Qwen3-VL 8B need on 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
The advisor's pick, Q4_K_M, is 5.0 GB of weights plus 2.42 GB of KV cache at 16k context (7.4 GB), against 7.0 GB usable.
Is the Qwen3-VL 8B speed on 8GB VRAM class (RTX 4060 8GB, 32GB RAM) measured or estimated?
Measured: our own llama-bench run on a rented GeForce RTX 4060, including runs with 4k to 16k tokens already in context.
What can I use instead of 8GB VRAM class (RTX 4060 8GB, 32GB RAM) for Qwen3-VL 8B?
It needs more memory than this hardware has; Mac mini / iMac M4 16GB, RTX 3060 12GB (32GB RAM), RTX 5070 12GB fit 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 8GB VRAM class (RTX 4060 8GB, 32GB RAM)?
It does not fit fully in GPU memory, so context is bounded by offload, not by this table.
Related
All models on 8GB VRAM class (RTX 4060 8GB, 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)