Can you run Qwen3.8 27B on RTX 4070 Ti SUPER?

Qwen3.8 27B Q4_K_M on the NVIDIA GeForce RTX 4070 Ti SUPER — verdict, VRAM math and estimated speed.

Yes, but slow
Quick answer

Yes, but slowly — Qwen3.8 27B spills past the RTX 4070 Ti SUPER. 16.5 GB weights at Q4_K_M vs 14.4 GB usable VRAM; ~5 tok/s est..

$ollama run qwen3.8:27b
VERDICT
Partial offload
EST. SPEED
~5 tok/s
WEIGHTS
16.5 GB Q4_K_M

VRAM math and speed are ModelFit engine estimates, not measurements. Commands are registry-verified Ollama tags.

Cite this page: ModelFit, Qwen3.8 27B on RTX 4070 Ti SUPER, https://modelfit.io/can-i-run/qwen3.8-27b-q4-on-rtx-4070-ti-super/, updated August 2026, CC BY 4.0.

Last updated: August 16, 2026 · Editor: ModelFit Team

VRAM
16 GB (14.4 usable)
Model weights
16.5 GB Q4_K_M
Est. speed
~5 tok/s
First token
~1.3s

Memory math: weights + context vs budget

Weights take 16.5 GB. Context costs extra KV-cache on top — this is where long-context sessions break on cards that technically fit the weights.

ContextKV-cacheTotalFits
8k tokens0.5 GB17.0 GBOver
16k tokens1.0 GB17.5 GBOver
32k tokens2.0 GB18.5 GBOver
64k tokens4.0 GB20.5 GBOver
128k tokens8.0 GB24.5 GBOver

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.

Upgrade path

The cheapest tracked card that runs Qwen3.8 27B comfortably is the AMD Radeon RX 7900 XT (20 GB VRAM).

See the RX 7900 XT page

Qwen3.8 27B on RTX 4070 Ti SUPER: FAQ

Can the NVIDIA GeForce RTX 4070 Ti SUPER run Qwen3.8 27B?

Barely. Qwen3.8 27B (Q4_K_M) needs about 16.5 GB but the RTX 4070 Ti SUPER has 14.4 GB usable VRAM, so part of the model spills to system RAM and speed drops to at roughly 5 tokens/sec (est.).

How much VRAM does Qwen3.8 27B need?

About 16.5 GB for the weights at Q4_K_M, plus KV-cache for context: roughly 1.0 GB extra at 16k tokens. The RTX 4070 Ti SUPER budget is 14.4 GB (16 GB x 90%).

What is the best quantization of Qwen3.8 27B for the RTX 4070 Ti SUPER?

Stick with the Q4_K_M build at 16.5 GB — every heavier quant exceeds the 14.4 GB usable VRAM.

What GPU do I need to run Qwen3.8 27B comfortably?

The cheapest tracked card that runs Qwen3.8 27B (Q4_K_M) fully in VRAM is the AMD Radeon RX 7900 XT (20 GB).