Advisor / Can it run? / Qwen3.5 4B on RTX 5070 12GB

Can RTX 5070 12GB run Qwen3.5 4B?

Measured results for Qwen3.5 4B (4.66B dense) on RTX 5070 12GB, plus what fits and the commands to run it.

115.7generation tok/s (Q8_0 on llama.cpp CUDA, 128-token generation)
7724prompt tok/s (pp512)
5.1 GBQ8_0 + 16k f16 KV, fits fully
11 GBusable on this hardware

Short answer

Yes. Qwen3.5 4B Q8_0 fits fully on RTX 5070 12GB at 16k context, using 5.1 of 11.0 GB usable, and generates about 115.7 tokens/s in our own run. As the context fills it slows down: 105 tok/s with 16k tokens already in context (91% of the empty-context speed), measured. For Q8_0, our bandwidth-only estimate was ~83 tok/s against a measured median of ~116 (29% too pessimistic).

Measured generation speed by quantization

QuantGeneration tok/sRangePrompt tok/sRunsOrigin
Q4 class (median)154.5154.5–154.574491includes our runs
Q8 class (median)115.7115.7–115.777241includes our runs
Q4_K_M (our run, depth 0)154.53 repeats74493own measurement
Q8_0 (our run, depth 0)115.73 repeats77243own 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 5070 (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_M0154.5100%7449
Q4_K_M4k147.495%7121
Q4_K_M16k135.388%6021
Q8_00115.7100%7724
Q8_04k111.897%7406
Q8_016k10591%6236

Alternatives

Less memory that still fits Qwen3.5 4B

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

Faster for Qwen3.5 4B

Other models on RTX 5070 12GB, similar size

Memory and context limits

Q8_0 weights are 4.5 GB; the 16k f16 KV cache adds 0.59 GB. Largest context that fits in 11.0 GB usable:

KV cache typeMax context
f16128k
q8_0256k
q4_0512k

Native limit 256k, 1.01M with YaRN. Full KV cache table for Qwen3.5 4B.

Copy-paste commands

The advisor's pick for this hardware: Q8_0, 16k context.

llama.cpp
llama-server -hf unsloth/Qwen3.5-4B-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99
ollama
OLLAMA_CONTEXT_LENGTH=16384 ollama serve
ollama run hf.co/unsloth/Qwen3.5-4B-GGUF:Q8_0

FAQ

How fast is Qwen3.5 4B on RTX 5070 12GB?

About 115.7 tokens/s for generation (Q8_0 on llama.cpp CUDA, 128-token generation), 7724 tokens/s for prompt processing. Speed drops as the context fills; see the depth table.

How much memory does Qwen3.5 4B need on RTX 5070 12GB?

The advisor's pick, Q8_0, is 4.5 GB of weights plus 0.59 GB of KV cache at 16k context (5.1 GB), against 11.0 GB usable.

Is the Qwen3.5 4B speed on RTX 5070 12GB measured or estimated?

Measured: our own llama-bench run on a rented GeForce RTX 5070, including runs with 4k to 16k tokens already in context.

What can I use instead of RTX 5070 12GB for Qwen3.5 4B?

With less memory, 8GB VRAM class (RTX 4060 8GB, 32GB RAM) (~51 tok/s) also fit it fully. Faster: RTX 5080 16GB (~153 tok/s), RTX 5070 Ti 16GB (~143 tok/s), RTX 4090 24GB (~158 tok/s).

What is the maximum context for Qwen3.5 4B on RTX 5070 12GB?

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 RTX 5070 12GB · Qwen3.5 4B on all hardware · VRAM tier · all measured pages · leaderboard

Compare: RTX 4070 12GB vs RTX 5070 12GB