Can the RTX 6000D run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 6000D's 84 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 300 tok/s. There's room for up to FP16 if you want higher quality.

VRAM
84 GB
Needs at Q4
4.8 GB
Headroom
79.2 GB
Speed (Q4_K_M)
~300 tok/s

Every quantization

Qwen3.5 4B (4.7B) on 84 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M3 GB4.8 GBFits easily~300 tok/s
Q5_K_M3 GB4.8 GBFits easily~300 tok/s
Q8_05 GB6.8 GBFits easily~180 tok/s
FP169 GB10.8 GBFits easily~100 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 6000D's 1570 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 6000D

Frequently asked

Quick answers

Can the RTX 6000D run Qwen3.5 4B?

Yes. Qwen3.5 4B fits the RTX 6000D's 84 GB at Q4_K_M (about 4.8 GB needed), running at an estimated 300 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 6000D?

Roughly 300 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 6000D's 1570 GB/s memory bandwidth.