Can the RTX 5070 run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 5070's 12 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 128 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
12 GB
Needs at Q4
4.8 GB
Headroom
7.2 GB
Speed (Q4_K_M)
~128 tok/s

Every quantization

Qwen3.5 4B (4.7B) on 12 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M3 GB4.8 GBFits easily~128 tok/s
Q5_K_M3 GB4.8 GBFits easily~128 tok/s
Q8_05 GB6.8 GBFits easily~77 tok/s
FP169 GB10.8 GBTight~43 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5070's 672 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf unsloth/Qwen3.5-4B-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen3.5 4B details →

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Qwen3.5 4B on other hardware

Also runs on the RTX 5070

Frequently asked

Quick answers

Can the NVIDIA RTX 5070 run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 5070's 12 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 128 tok/s. There's room for up to FP16 if you want higher quality.

How much VRAM does Qwen3.5 4B need?

About 3 GB of weights at Q4_K_M, 5 GB at Q8_0 and 9 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

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

Roughly 128 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5070's 672 GB/s memory bandwidth.