Quick answer: The RTX 4060 Ti 16GB runs Flux.1 Dev at 1024x1024 without memory issues. The 8GB version struggles badly — it will offload to system RAM, slowing generation times to a crawl. If you own the 8GB variant, Flux is barely usable.
NVIDIA GeForce RTX 4060 Ti 16GB
16GB GDDR6The only 4060 Ti worth buying for Flux — 16GB VRAM clears the minimum bar comfortably.
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Why Flux is VRAM-hungry
Flux.1 Dev and Flux.1 Schnell are fundamentally different from older diffusion models. The transformer architecture is larger than SDXL, and the model checkpoint alone consumes around 10-12 GB of VRAM before the VAE and ControlNet even load. On a card with 8 GB, the framework immediately starts swapping data to system RAM using shared memory — this is slow and often results in incomplete generations on underpowered systems.
Flux at 1024x1024 with default settings requires:
| Component | VRAM Usage |
|---|---|
| Flux.1 Dev checkpoint (FP8) | ~10-12 GB |
| VAE decoder | ~1 GB |
| Conditioning / text encoder | ~1-2 GB |
| Working buffers | ~1-2 GB |
| Total at 1024x1024 | ~12-14 GB |
The 16GB RTX 4060 Ti clears this with 2-4 GB to spare. The 8GB card does not.
RTX 4060 Ti 16GB: What to expect
Running Flux.1 Dev on the RTX 4060 Ti 16GB is possible and practical, with caveats:
- Generation time: Expect 30-60 seconds per image at 1024x1024 with 20 steps. The narrow 128-bit memory bus is the bottleneck — GDDR6 bandwidth limits throughput compared to wider-bus cards like the RTX 4090.
- 1024x1024: Works well within 16 GB. Comfortable headroom.
- 1536x1024 or larger: Can push past 14 GB depending on the sampler. Still manageable but close to the limit.
- Flux + ControlNet: A single ControlNet adds 1-3 GB. Tight at 16 GB but usually works with careful settings.
- Flux + multiple LoRA: Fine. Each LoRA adds 100-500 MB, well within budget.
RTX 4060 Ti 8GB: The honest picture
The 8GB version is not designed for Flux. You can technically run it using sequential offloading or the --lowvram flag in ComfyUI/AUTO1111, but:
- Generation times jump to 3-10 minutes per image
- Output quality can be inconsistent
- Larger resolutions simply fail or require aggressive quantization (Flux Schnell q4/q5 GGUF)
If you only care about running Flux occasionally at low resolution and you already own the 8GB card, GGUF-quantized models (Q4/Q5) reduce VRAM to around 6-8 GB and bring generation back to reasonable speeds. But buying an 8GB card specifically for Flux in 2026 would be a mistake.
VRAM optimization tips for the 4060 Ti 16GB
Getting the most out of 16 GB on Flux:
- Use fp8 or GGUF Flux models — fp8 versions of Flux.1 Dev reduce VRAM from 12 GB to around 8-9 GB, freeing up headroom for ControlNet or larger resolutions.
- Enable tiled VAE decoding in ComfyUI to reduce VAE peak usage.
- Unload the text encoder after conditioning — saves 1-2 GB during the denoising pass.
- Close background apps — browsers and other GPU-accelerated software eat into the 16 GB.
- Use Flux Schnell for iteration — 4-step model, much faster for testing prompts.
Which GPU should YOU buy?
Own the 4060 Ti 16GB? You are set for Flux. Not the fastest option, but it works without workarounds.
Own the 4060 Ti 8GB? Use GGUF quantized Flux models and accept slower speeds. For a dedicated Flux machine, upgrade to at least 16 GB VRAM.
Shopping for a Flux GPU around $425? The RTX 4060 Ti 16GB is the cheapest route to a comfortable Flux experience. If you can stretch to $875+, the RTX 5070 (12 GB GDDR7) is faster despite having less VRAM, thanks to much higher memory bandwidth.
Want zero compromise? The RTX 4090 (24 GB) or RTX 5080 (16 GB GDDR7) deliver full-speed Flux with room for complex ControlNet stacks.
Common mistakes to avoid
- Buying the 8GB version to save $50. The performance gap for Flux is enormous. The 8GB card turns a 30-second generation into a 5-minute wait.
- Running Flux without fp8 or GGUF optimization. Full fp16 Flux models push 12-13 GB, leaving almost no headroom on the 16 GB card.
- Expecting RTX 4060 Ti speeds to match newer cards. The 128-bit bus is a real ceiling. The RTX 5070 generates Flux images 2-3x faster despite the same VRAM.
Final verdict
| Variant | Flux at 1024x1024 | Speed | Verdict |
|---|---|---|---|
| RTX 4060 Ti 16GB | Yes, comfortable | 30-60s/img | Capable |
| RTX 4060 Ti 8GB | Barely, with GGUF | 3-10 min/img | Avoid for Flux |
The 16GB RTX 4060 Ti is a legitimate Flux card — not fast, but not broken either. If Flux is your primary workload, it gets the job done. For faster generation, see our Best GPU for Flux guide, and if you want to understand exactly how much VRAM Flux needs at different settings, check How Much VRAM for Flux. For broader ComfyUI workflows beyond Flux, our Best GPU for ComfyUI guide covers the full picture.
Check RTX 4060 Ti 8GB price on Amazon→Buy on Shopee SG→FAQ: Flux on the RTX 4060 Ti
How much VRAM does Flux Schnell need?
Flux Schnell in quantized GGUF form (Q4 or Q5) needs roughly 6-8 GB of VRAM, which is why it is the practical option for 8 GB cards. The full-precision version has similar requirements to Flux Dev — around 12-14 GB total at 1024x1024 once the VAE, text encoder, and working buffers load. Schnell is also a 4-step model, so it is the faster choice for iterating on prompts.
Can the RTX 4060 Ti 8GB run Flux?
Barely. The 8 GB card has to offload to system RAM using sequential offloading or low-VRAM flags, which pushes generation times to roughly 3-10 minutes per image and can produce inconsistent output. Quantized GGUF models (Q4/Q5) bring VRAM down to around 6-8 GB and make it usable at low resolutions, but buying the 8GB version specifically for Flux in 2026 is a mistake.
How much VRAM does Flux Dev need at 1024x1024?
Roughly 12-14 GB in total with default fp16 settings: the checkpoint alone consumes around 10-12 GB, and the VAE, text encoder, and working buffers add a few more. Switching to an fp8 version reduces the model footprint to around 8-9 GB, freeing headroom for ControlNet or larger resolutions on a 16 GB card.
Is the RTX 4060 Ti 16GB a good GPU for Flux?
Yes — it runs Flux.1 Dev at 1024x1024 comfortably, with generation times of roughly 30-60 seconds per image at 20 steps. The narrow 128-bit memory bus makes it noticeably slower than newer cards like the RTX 5070, but at around $425 it is the cheapest route to a Flux setup that works without workarounds.