Can you run Qwen3.5 35B-A3B Instruct on RTX 3090?
Qwen3.5 35B-A3B Instruct Q4_K_M on the NVIDIA GeForce RTX 3090: verdict, VRAM math and estimated speed.
Yes, the NVIDIA GeForce RTX 3090 runs Qwen3.5 35B-A3B Instruct. 20 GB weights at Q4_K_M vs 21.6 GB usable VRAM; ~71 tok/s est. (ModelFit, 2026).
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Qwen3.5 35B-A3B Instruct on RTX 3090, https://modelfit.io/can-i-run/qwen3.5-35b-a3b-q4-on-rtx-3090/, updated September 2026, CC BY 4.0.
Last updated: September 18, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Qwen3.5 35B-A3B Instruct on the RTX 3090, in order of what usually breaks first.
Quant explorer
Switch between the quality-gated builds tracked for this exact combo. Q4_K_M stays the default recommendation; heavier Q6/Q8 builds only appear when their exact Ollama tags are registry-verified.
Why no 2-bit builds: Q1/Q2-class quants can look attractive in a memory table, but their quality loss is large enough that ModelFit excludes them from rankings instead of inflating the catalog with junk options. Usable VRAM: 21.6 GB.
Workload verdicts
Qwen3.5 35B-A3B Instruct on the RTX 3090, graded per use case from the model's registry-verified tuning and the engine's speed estimate for this exact combo.
Grades combine tag-verified tuning (a model not built for the workload caps at C) with the tok/s floor each workload needs to feel usable. A combo that partially offloads caps at C; one that does not fit is D everywhere.
Memory math: weights + context vs budget
Weights take 20 GB. Context costs extra KV-cache on top — this is where long-context sessions break on cards that technically fit the weights.
The model leaves about 1.3 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
See how fast it feels
A deterministic typing simulation for Qwen3.5 35B-A3B Instruct: first token ~1.0s (instant prefill), then ~71 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Qwen3.5 35B-A3B Instruct on RTX 3090: FAQ
Can the NVIDIA GeForce RTX 3090 run Qwen3.5 35B-A3B Instruct?
Yes. Qwen3.5 35B-A3B Instruct (Q4_K_M) loads in about 20 GB and the RTX 3090 offers 21.6 GB of usable VRAM, leaving headroom for context. Expect at roughly 71 tokens/sec (est.).
How much VRAM does Qwen3.5 35B-A3B Instruct need?
About 20 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 0.3 GB extra at 16k tokens. The RTX 3090 budget is 21.6 GB (24 GB x 90%).
What is the best quantization of Qwen3.5 35B-A3B Instruct for the RTX 3090?
Stick with the Q4_K_M build at 20 GB — every heavier quant exceeds the 21.6 GB usable VRAM.
What GPU do I need to run Qwen3.5 35B-A3B Instruct comfortably?
The RTX 3090 already runs Qwen3.5 35B-A3B Instruct comfortably. Larger cards only buy you longer context or a heavier quant.
How much context can Qwen3.5 35B-A3B Instruct use on the RTX 3090?
Up to 64k tokens stay fully in VRAM (21.3 GB total with the KV cache). At 128k tokens the total reaches 22.5 GB and tips over the budget. Quantizing the KV cache to q8_0 roughly halves the KV column and buys back about one context tier.
Is Qwen3.5 35B-A3B Instruct on the RTX 3090 fast enough for coding agents?
Yes: the engine estimates ~71 tok/s against the ~30 tok/s an agentic loop needs to feel responsive, and Qwen3.5 35B-A3B Instruct is tuned for tool-calling.