Advisor / Can it run? / Qwen3.5 9B on RTX 3090 24GB

Can RTX 3090 24GB run Qwen3.5 9B?

Measured results for Qwen3.5 9B (9.65B dense) on RTX 3090 24GB, plus what fits and the commands to run it.

85.7generation tok/s (Q8_0 on llama.cpp CUDA, 128-token generation)
4788prompt tok/s (pp512)
10.1 GBQ8_0 + 16k f16 KV, fits fully
23 GBusable on this hardware

Short answer

Yes. Qwen3.5 9B Q8_0 fits fully on RTX 3090 24GB at 16k context, using 10.1 of 23.0 GB usable, and generates about 85.7 tokens/s in our own run. As the context fills it slows down: 81.5 tok/s with 16k tokens already in context (95% of the empty-context speed), measured. For Q8_0, our bandwidth-only estimate was ~54 tok/s against a measured median of ~86 (37% too pessimistic).

Measured generation speed by quantization

QuantGeneration tok/sRangePrompt tok/sRunsOrigin
Q4 class (median)122.1122.1–122.146321includes our runs
Q8 class (median)85.785.7–85.747881includes our runs
Q4_K_M (our run, depth 0)122.13 repeats46323own measurement
Q8_0 (our run, depth 0)85.73 repeats47883own 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 3090 (flash attention on, 3 repeats): generation and prompt speed with the given number of tokens already in the context.

QuantContext depthGeneration tok/svs depth 0Prompt tok/s
Q4_K_M0122.1100%4632
Q4_K_M4k120.298%4545
Q4_K_M16k113.993%4027
Q4_K_M32k106.287%3413
Q4_K_M64k93.877%2740
Q4_K_M128k75.862%1904
Q8_0085.7100%4788
Q8_04k84.899%4719
Q8_016k81.595%4217

Alternatives

Less memory that still fits Qwen3.5 9B

Smaller memory usually means a cheaper card or Mac (we do not list prices).

Faster for Qwen3.5 9B

Other models on RTX 3090 24GB, similar size

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 23.0 GB usable:

KV cache typeMax context
f16256k
q8_0512k
q4_01M

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.cpp
llama-server -hf unsloth/Qwen3.5-9B-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
ollama
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 RTX 3090 24GB?

About 85.7 tokens/s for generation (Q8_0 on llama.cpp CUDA, 128-token generation), 4788 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.

How much memory does Qwen3.5 9B need on RTX 3090 24GB?

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 23.0 GB usable.

Is the Qwen3.5 9B speed on RTX 3090 24GB measured or estimated?

Measured: our own llama-bench run on a rented GeForce RTX 3090, including runs with 4k to 128k tokens already in context.

What can I use instead of RTX 3090 24GB 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 5090 32GB (~156 tok/s), RTX PRO 6000 96GB (~153 tok/s).

What is the maximum context for Qwen3.5 9B on RTX 3090 24GB?

With an f16 KV cache about 256k tokens, 512k with q8_0 and 1M with q4_0, bounded by the model's own limit.

Related

All models on RTX 3090 24GB · Qwen3.5 9B on all hardware · VRAM tier · all measured pages · leaderboard

Compare: RTX 3090 24GB vs RTX 4090 24GB · RTX 3090 24GB vs RTX 5090 32GB · RTX 3090 24GB vs AMD RX 7900 XTX 24GB