Can you run Qwen3.5 35B-A3B Instruct on RTX 5080?
Qwen3.5 35B-A3B Instruct Q4_K_M on the NVIDIA GeForce RTX 5080: verdict, VRAM math and estimated speed.
Yes, but slowly: Qwen3.5 35B-A3B Instruct spills past the RTX 5080. 20 GB weights at Q4_K_M vs 14.4 GB usable VRAM; ~57 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 5080, https://modelfit.io/can-i-run/qwen3.5-35b-a3b-q4-on-rtx-5080/, 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 5080, 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: 14.4 GB.
Workload verdicts
Qwen3.5 35B-A3B Instruct on the RTX 5080, 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.
This configuration exceeds the comfortable VRAM budget by about 5.9 GB. Expect offload pressure or a shorter safe 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 ~57 tokens/sec.
ModelFit engine estimate, not a measured benchmark. Real speed varies with prompt length, thermals, runtime, and KV-cache settings.
Upgrade path
The cheapest tracked card that runs Qwen3.5 35B-A3B Instruct comfortably is the AMD Radeon RX 7900 XTX (24 GB VRAM).
See the RX 7900 XTX pageQwen3.5 35B-A3B Instruct on RTX 5080: FAQ
Can the NVIDIA GeForce RTX 5080 run Qwen3.5 35B-A3B Instruct?
Barely. Qwen3.5 35B-A3B Instruct (Q4_K_M) needs about 20 GB but the RTX 5080 has 14.4 GB usable VRAM, so part of the model spills to system RAM and speed drops to at roughly 57 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 5080 budget is 14.4 GB (16 GB x 90%).
What is the best quantization of Qwen3.5 35B-A3B Instruct for the RTX 5080?
Stick with the Q4_K_M build at 20 GB — every heavier quant exceeds the 14.4 GB usable VRAM.
What GPU do I need to run Qwen3.5 35B-A3B Instruct comfortably?
The cheapest tracked card that runs Qwen3.5 35B-A3B Instruct (Q4_K_M) fully in VRAM is the AMD Radeon RX 7900 XTX (24 GB).
How much context can Qwen3.5 35B-A3B Instruct use on the RTX 5080?
None worth having — the 20 GB of weights alone exceed the 14.4 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is Qwen3.5 35B-A3B Instruct on the RTX 5080 fast enough for coding agents?
Yes: the engine estimates ~57 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.