Can 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) run Qwen3.5 9B?
Measured results for Qwen3.5 9B (9.65B dense) on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM), plus what fits and the commands to run it.
Short answer
Yes. Qwen3.5 9B Q8_0 fits fully on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) at 16k context, using 10.1 of 15.0 GB usable, and generates about 30.4 tokens/s in our own run. As the context fills it slows down: 28.7 tok/s with 16k tokens already in context (94% of the empty-context speed), measured. For Q8_0, our bandwidth-only estimate was ~17 tok/s against a measured median of ~30 (45% too pessimistic).
Measured generation speed by quantization
| Quant | Generation tok/s | Range | Prompt tok/s | Runs | Origin |
|---|---|---|---|---|---|
| Q4 class (median) | 48.3 | 48.3–48.3 | 3191 | 1 | includes our runs |
| Q8 class (median) | 30.4 | 30.4–30.4 | 3360 | 1 | includes our runs |
| Q4_K_M (our run, depth 0) | 48.3 | 3 repeats | 3191 | 3 | own measurement |
| Q8_0 (our run, depth 0) | 30.4 | 3 repeats | 3360 | 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 Ti 16GB (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 | 48.3 | 100% | 3191 |
| Q4_K_M | 4k | 47.2 | 98% | 3092 |
| Q4_K_M | 16k | 44.1 | 91% | 2533 |
| Q8_0 | 0 | 30.4 | 100% | 3360 |
| Q8_0 | 4k | 29.9 | 99% | 3249 |
| Q8_0 | 16k | 28.7 | 94% | 2610 |
Alternatives
Less memory that still fits Qwen3.5 9B
Smaller memory usually means a cheaper card or Mac (we do not list prices).
- 8GB VRAM class (RTX 4060 8GB, 32GB RAM): Q4_K_M, 7 GB usable, ~46 tok/s measured
- Mac mini / iMac M4 16GB: Q4_K_M, 11 GB usable, ~14 tok/s scaled from measured
- RTX 5070 12GB: Q8_0, 11 GB usable, ~68 tok/s measured
Faster for Qwen3.5 9B
- RTX 4070 12GB: Q8_0, 11 GB usable, ~51 tok/s measured
- RTX 3060 12GB (32GB RAM): Q8_0, 11 GB usable, ~37 tok/s measured
- RTX 5080 16GB: Q8_0, 15 GB usable, ~92 tok/s measured
Other models on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM), similar size
- Ornith 1.5 9B (hardware page): Q8_0, fits fully, ~30 tok/s (scaled from measured)
- Qwen3-VL 8B: Q8_0, fits fully, ~33 tok/s (measured)
- Gemma 4 E4B (hardware page): Q8_0, fits fully, ~35 tok/s (scaled from measured)
- Gemma 4 12B: Q8_0, fits fully, ~21 tok/s (measured)
Memory and context limits
Q8_0 weights are 9.5 GB; the 16k f16 KV cache adds 0.59 GB. Largest context that fits in 15.0 GB usable:
| KV cache type | Max context |
|---|---|
| f16 | 128k |
| q8_0 | 256k |
| q4_0 | 512k |
Native limit 256k, 1.01M with YaRN. Full KV cache table for Qwen3.5 9B.
Copy-paste commands
The advisor's pick for this hardware: Q8_0, 16k context.
llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
OLLAMA_CONTEXT_LENGTH=16384 ollama serve ollama run hf.co/unsloth/Qwen3.5-9B-GGUF:Q8_0
FAQ
How fast is Qwen3.5 9B on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
About 30.4 tokens/s for generation (Q8_0 on llama.cpp CUDA, 128-token generation), 3360 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.
How much memory does Qwen3.5 9B need on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
The advisor's pick, Q8_0, is 9.5 GB of weights plus 0.59 GB of KV cache at 16k context (10.1 GB), against 15.0 GB usable.
Is the Qwen3.5 9B speed on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) measured or estimated?
Measured: our own llama-bench run on a rented GeForce RTX 4060 Ti 16GB, including runs with 4k to 16k tokens already in context.
What can I use instead of 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) for Qwen3.5 9B?
With less memory, 8GB VRAM class (RTX 4060 8GB, 32GB RAM) (~46 tok/s), Mac mini / iMac M4 16GB (~14 tok/s), RTX 5070 12GB (~68 tok/s) also fit it fully. Faster: RTX 4070 12GB (~51 tok/s), RTX 3060 12GB (32GB RAM) (~37 tok/s), RTX 5080 16GB (~92 tok/s).
What is the maximum context for Qwen3.5 9B on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)?
With an f16 KV cache about 128k tokens, 256k with q8_0 and 512k with q4_0, bounded by the model's own limit.
Related
All models on 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · Qwen3.5 9B on all hardware · VRAM tier · all measured pages · leaderboard
Compare: RTX 5070 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) · RTX 5060 Ti 16GB vs 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM)