Can you run Qwen3.8 27B (Q8) on RTX 4060 Ti?
Qwen3.8 27B (Q8) Q8_0 on the NVIDIA GeForce RTX 4060 Ti: verdict, VRAM math and estimated speed.
No, Qwen3.8 27B (Q8) does not realistically run on the NVIDIA GeForce RTX 4060 Ti. 27.1 GB weights at Q8_0 vs 14.4 GB usable VRAM; ~1 tok/s est. (ModelFit, 2026).
→ Cheapest tracked card that runs it: AMD Ryzen AI Max+ 395 (Strix Halo) (110 GB)
VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.
Cite this page: ModelFit, Qwen3.8 27B (Q8) on RTX 4060 Ti, https://modelfit.io/can-i-run/qwen3.8-27b-q8-on-rtx-4060-ti/, updated September 2026, CC BY 4.0.
Last updated: September 24, 2026 · Editor: ModelFit Team
What limits this combo
The four constraints the fit engine checks for Qwen3.8 27B (Q8) on the RTX 4060 Ti, in order of what usually breaks first.
Where the RTX 4060 Ti sits for Qwen3.8 27B (Q8)
The RTX 4060 Ti cannot hold Qwen3.8 27B (Q8) in VRAM: 27.1 GB of weights against 14.4 GB usable. The cheapest tracked card that runs Qwen3.8 27B (Q8) fully in VRAM is the RTX 5090 (32 GB, ~32 tok/s est.). Context ceiling on this card: none; on the RTX 5090: 16k.
Same engine as the verdict above: 90% of VRAM usable, KV-cache at fp16, bandwidth-derived tok/s. Verdicts of the other cards are computed for Qwen3.8 27B (Q8) Q8_0 exactly.
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
Qwen3.8 27B (Q8) on the RTX 4060 Ti, 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 27.1 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 14 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 Qwen3.8 27B (Q8): first token ~7.0s (slow prefill), then ~1 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 Qwen3.8 27B (Q8) comfortably is the AMD Ryzen AI Max+ 395 (Strix Halo) (110 GB unified memory).
See the Ryzen AI Max+ 395 pageQwen3.8 27B (Q8) on RTX 4060 Ti: FAQ
Can the NVIDIA GeForce RTX 4060 Ti run Qwen3.8 27B (Q8)?
Not realistically. Qwen3.8 27B (Q8) (Q8_0) needs about 27.1 GB against 14.4 GB usable VRAM on the RTX 4060 Ti; even with heavy CPU offload it would run at unusable speed.
How much VRAM does Qwen3.8 27B (Q8) need?
About 27.1 GB for the weights at Q8_0, plus KV-cache for context: roughly 1.0 GB extra at 16k tokens. The RTX 4060 Ti budget is 14.4 GB (16 GB x 90%).
What is the best quantization of Qwen3.8 27B (Q8) for the RTX 4060 Ti?
Stick with the Q8_0 build at 27.1 GB; every heavier quant exceeds the 14.4 GB usable VRAM.
What GPU do I need to run Qwen3.8 27B (Q8) comfortably?
The cheapest tracked card that runs Qwen3.8 27B (Q8) (Q8_0) fully in VRAM is the AMD Ryzen AI Max+ 395 (Strix Halo) (110 GB).
How much context can Qwen3.8 27B (Q8) use on the RTX 4060 Ti?
None worth having: the 27.1 GB of weights alone exceed the 14.4 GB usable VRAM, so the model only runs with system-RAM offload and short prompts.
Is Qwen3.8 27B (Q8) on the RTX 4060 Ti fast enough for coding agents?
No: the model does not realistically fit this card, so agentic loops are out of the question on it.