FLUX GGUF Quantization: Run FLUX Models on 8GB VRAM Cards

I remember the exact moment I realized FLUX was going to be a problem for my setup. It was a Friday night last year, I had just downloaded the full FLUX.1 Dev model, loaded it into ComfyUI, and watched my RTX 3060 12GB choke and crash within seconds. The model wanted more VRAM than I had, and that was the end of my evening. I spent the rest of that night reading about quantization methods, and what I found completely changed how I approach AI image generation on consumer hardware.

If you have an 8GB VRAM card, you've probably felt this pain. FLUX models produce some of the best AI-generated images available right now, but the full-precision versions demand 12GB or more of VRAM just to load. That locks out a massive number of people running RTX 3060s, RTX 4060s, and similar cards. GGUF quantization is the solution, and honestly, the quality you can get from a properly quantized FLUX model is shockingly close to the original.

Quick Answer: GGUF quantization compresses FLUX models from their original size (around 23GB for FLUX.1 Dev) down to as small as 5-7GB, making them runnable on 8GB VRAM GPUs. The best balance for most users is Q5_K_S quantization, which preserves roughly 95% of the original quality while fitting comfortably in 8GB VRAM. You can set this up in ComfyUI using the GGUF loader nodes from city96's ComfyUI-GGUF extension. The quality loss at Q5 is minimal for most use cases, and even Q4 quantization produces genuinely usable images.

If you're still deciding on hardware for AI image generation, my best GPU for AI guide breaks down the current options at every price point. But if you already have an 8GB card and want to make FLUX work on it today, keep reading.

What Is GGUF Quantization and Why Does It Matter for FLUX?

Before we dive into the practical setup, it helps to understand what we're actually doing when we quantize a model. I'm not going to bore you with the deep math, but knowing the basics will help you make better decisions about which quantization level to choose.

GGUF stands for GPT-Generated Unified Format, and it was originally developed by the llama.cpp community for compressing large language models. The format has since been adapted for diffusion models like FLUX, and it works remarkably well. At its core, quantization reduces the precision of the numbers (weights) stored in a neural network. A full-precision FLUX model stores its weights in 16-bit floating point format. Quantization converts those weights to lower precision formats like 8-bit, 5-bit, or even 4-bit integers.

The clever part is that not all weights are treated equally. Modern quantization schemes like those used in GGUF use mixed precision, keeping the most important weights at higher precision while aggressively compressing the less critical ones. That's what the "K" in formats like Q4_K_S means. It uses a k-quant method that groups weights and assigns different precision levels based on importance. The "_S" suffix means "small," referring to a smaller group size that uses slightly less memory at the cost of marginally lower quality compared to "_M" (medium) variants.

Here's something that surprised me when I first started testing this. I expected quantized models to produce noticeably worse images. Like, I was bracing for muddy textures and weird artifacts. But when I ran my first Q5 FLUX generation and compared it side by side with the full precision output, I genuinely could not tell which was which on most prompts. It took very specific test cases, like generating images with small text or extremely fine fabric patterns, before the differences became visible.

Side-by-side comparison of the same prompt at Q4_K_S, Q5_K_S, Q8_0, and full precision FLUX.1 Dev. The differences are subtle at normal viewing distances.

How Do the Different Quantization Levels Compare?

This is the question everyone asks, and the answer depends on what you're optimizing for. I've spent the better part of a month running systematic comparisons across all the common quantization levels, and here's what I found.

Q4_K_S (4-bit quantization)

File size is roughly 6-7GB for FLUX.1 Dev. This is the most aggressive quantization that still produces genuinely usable images. You will notice some quality loss, particularly in fine details, skin textures, and areas with subtle color gradients. Text generation in images takes a significant hit. But for general purpose image generation, compositions, landscapes, character portraits at standard viewing distances, Q4 is surprisingly capable.

I've been using Q4 on my RTX 4060 8GB when I want to stack a LoRA on top of FLUX, because the smaller model footprint leaves enough VRAM headroom to load a LoRA without crashing. That's a tradeoff I'm willing to make most of the time, and it's one that opens up creative possibilities that would otherwise be completely off the table on an 8GB card.

VRAM usage sits around 6-7GB during generation at 1024x1024, which gives you a comfortable buffer on an 8GB card. You can sometimes push to 1280x1280 if you enable tiled VAE decoding.

Q5_K_S (5-bit quantization)

File size is roughly 8-9GB for FLUX.1 Dev. This is the sweet spot for most 8GB VRAM users, and it's the quantization level I recommend to almost everyone. The quality retention compared to Q4 is noticeably better, especially in areas like hair detail, fabric textures, and facial features. The jump from 4-bit to 5-bit might not sound like much, but in practice, Q5_K_S closes about half the gap between Q4 and full precision.

VRAM usage is tighter at around 7-8GB during generation, which means you need to be more careful about resolution and batch size. At 1024x1024, most 8GB cards handle it fine. But you won't have much room for LoRAs on top, and going above 1024x1024 will likely require tiled VAE decoding or other VRAM-saving tricks.

Hot take: Q5_K_S on an 8GB card produces better results than running full-precision FLUX.1 Schnell on the same card, because you get the quality advantage of the Dev model's more refined inference process even with the quantization penalty. I know that's debatable, but I've tested it extensively, and I stand by it.

Q8_0 (8-bit quantization)

File size is roughly 12-13GB for FLUX.1 Dev. You're not running this on an 8GB card unless you're doing aggressive offloading to system RAM, which kills performance. I'm including it here because some people with 8GB cards consider it with CPU offloading enabled. My advice: don't bother. The speed penalty is brutal. A generation that takes 30 seconds at Q5 might take 5-10 minutes with Q8 and CPU offloading. If you have a 12GB card like the RTX 3060, Q8 is the clear winner though, as the quality is virtually identical to full precision.

Quick Comparison Table

These numbers are approximate and vary slightly depending on your ComfyUI version, operating system, and what else is consuming VRAM on your system.

How Do You Set Up GGUF FLUX in ComfyUI?

Getting GGUF FLUX running in ComfyUI is surprisingly straightforward once you know which pieces you need. I'll walk you through the entire process from scratch.

Step 1: Install the GGUF Extension

The key extension you need is city96's ComfyUI-GGUF. This adds the necessary loader nodes that can read GGUF-formatted models. If you're using ComfyUI Manager, you can search for "GGUF" and install it directly. Otherwise, clone the repo into your custom_nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/city96/ComfyUI-GGUF.git

Restart ComfyUI after installation. You should see new nodes available in your node menu under the "GGUF" category.

Step 2: Download Quantized FLUX Models

The most reliable source for quantized FLUX models is city96's HuggingFace repository. You'll find every quantization level available. Download the one that matches your VRAM budget. For 8GB cards, grab either flux1-dev-Q4_K_S.gguf or flux1-dev-Q5_K_S.gguf.

Place the downloaded file in your ComfyUI/models/unet/ folder. Not the checkpoints folder, the unet folder. This trips people up constantly.

ComfyUI/
  models/
    unet/
      flux1-dev-Q5_K_S.gguf    <-- put it here
    clip/
      t5xxl_fp8_e4m3fn.safetensors
      clip_l.safetensors
    vae/
      ae.safetensors

Step 3: Get the Text Encoders and VAE

FLUX uses two text encoders: CLIP-L and T5-XXL. For 8GB VRAM setups, you absolutely need the fp8 version of T5-XXL, because the full precision version is about 9.5GB by itself. Download t5xxl_fp8_e4m3fn.safetensors from the ComfyUI community resources or HuggingFace. CLIP-L is small enough that the regular version works fine.

For the VAE, use the standard FLUX VAE (ae.safetensors). Some people use an fp16 version, which works fine. The VAE is relatively small and doesn't eat much VRAM.

Step 4: Build the ComfyUI Workflow

Here's where it all comes together. Instead of the standard CheckpointLoader node, you'll use the UnetLoaderGGUF node from the GGUF extension. The workflow structure looks like this:

UnetLoaderGGUF → KSampler
DualCLIPLoader → CLIPTextEncode (positive) → KSampler
                → CLIPTextEncode (negative) → KSampler
VAELoader → VAEDecode → SaveImage
EmptyLatentImage → KSampler

The critical settings in the UnetLoaderGGUF node:

• unet_name: Select your GGUF file (e.g., flux1-dev-Q5_K_S.gguf)

For the DualCLIPLoader:

• clip_name1: t5xxl_fp8_e4m3fn.safetensors
• clip_name2: clip_l.safetensors
• type: flux

One thing I learned the hard way is that the node type matters. If you accidentally use the standard UnetLoader instead of UnetLoaderGGUF, ComfyUI will try to load the file as a regular safetensors model and either crash or throw a cryptic error. I spent an embarrassing amount of time debugging this the first time I set everything up because I didn't read the node name carefully enough. Don't make the same mistake.

A clean ComfyUI workflow for GGUF FLUX generation. Note the UnetLoaderGGUF node replacing the standard checkpoint loader.

What Are the Best VRAM Optimization Tricks for 8GB Cards?

Even with a quantized model, 8GB VRAM is tight. Here are the techniques I use daily to squeeze the most out of my setup. These aren't theoretical suggestions, as every single one of these comes from my actual workflow.

Tiled VAE Decoding

The VAE decode step is a VRAM spike that can crash your generation even if the model itself loaded fine. Tiled VAE decoding breaks the decode step into smaller tiles, dramatically reducing peak VRAM usage. In ComfyUI, you can use the VAEDecodeTiled node instead of the standard VAEDecode. Set the tile size to 512 for maximum VRAM savings, or 768 if you want slightly faster decoding with a bit more VRAM usage.

This one trick alone has saved more of my generations from crashing than any other optimization. I literally never use the standard VAE decode anymore on my 8GB card.

Aggressive Memory Management

ComfyUI has built-in flags for low-VRAM situations. Launch ComfyUI with the --lowvram flag to enable aggressive memory management:

python main.py --lowvram

This tells ComfyUI to keep as little as possible in VRAM and swap things to system RAM between steps. There's a performance penalty, maybe 20-30% slower generation times, but the tradeoff is that things actually work instead of crashing. For really tight situations, there's also --novram which offloads everything to CPU and only moves the active computation to GPU, but at that point you're looking at very slow generation times.

Close Everything Else

This sounds obvious, but I can't tell you how many times someone has asked me why their 8GB card keeps running out of memory, and it turns out they have Chrome open with 40 tabs, Discord running, and a game minimized in the background. Your GPU's VRAM is shared with your display output and any other GPU-accelerated application. On Windows, the desktop compositor alone uses a few hundred MB. Close everything you don't need before starting a generation session. Even your browser can eat 200-500MB of VRAM depending on what tabs you have open.

Resolution Management

At Q5 quantization on an 8GB card, you're comfortable at 1024x1024. If you need larger images, generate at 1024x1024 and upscale afterward using a separate pass. Trying to generate at 1536x1536 or higher with a quantized FLUX model on 8GB will almost certainly fail. I use a two-step workflow: generate at 1024x1024 with FLUX GGUF, then run the output through a lightweight upscaler like RealESRGAN 4x in a separate workflow. This approach actually gives better results than trying to generate at higher resolution directly, because the upscaler is specifically trained for the task.

Over at Apatero.com, I've been testing these VRAM optimization techniques across dozens of different hardware configurations, and the 1024-then-upscale workflow consistently produces the best results per VRAM dollar spent.

Can You Use LoRAs with GGUF FLUX Models?

Yes, and this is where things get really interesting. The GGUF extension supports LoRA loading on top of quantized models, which means you can run specialized styles and concepts even on 8GB cards. But there are some important caveats.

When you load a LoRA on top of a quantized model, the LoRA weights add to your VRAM usage. A typical FLUX LoRA is anywhere from 50MB to 500MB, with most sitting around 150-350MB. That means with Q5 quantization already using around 7.5GB, you have maybe 500MB of headroom for LoRAs on an 8GB card. That's enough for one small to medium LoRA, but not much more.

If you want to use larger LoRAs or multiple LoRAs simultaneously, drop down to Q4 quantization. The lower base VRAM usage gives you 1-1.5GB of breathing room, which is enough for two or three LoRAs in most cases.

I've been getting fantastic results combining Q4 FLUX with some of the ultra real FLUX LoRAs that have come out recently. The combination of a quantized base model and a focused LoRA can produce images that genuinely rival what you'd get from the full-precision model, because the LoRA adds back specialized quality in the areas that matter for your specific use case.

Hot take: for style-specific generation, Q4 plus a good LoRA actually outperforms full-precision FLUX without a LoRA. The LoRA's focused training data compensates for the quantization loss, and then some. I've tested this with portrait LoRAs, landscape LoRAs, and several anime-style LoRAs, and the pattern holds consistently.

How Does FLUX GGUF Compare to FLUX.1 Schnell for Low-VRAM Users?

This is a comparison I see debated constantly in Discord servers and Reddit threads, and most people get it wrong. Let me break it down based on my actual testing.

FLUX.1 Schnell is the fast, distilled version of FLUX designed for quick generation. It produces decent images in just 1-4 steps. FLUX.1 Dev is the full model, producing higher quality images but requiring 20-50 steps. When you quantize FLUX.1 Dev to GGUF format, you're trading model precision for accessibility, but you keep the fundamental quality advantage of the Dev model's architecture.

Here's what matters in practice. A Q5 FLUX.1 Dev GGUF model at 20 steps produces images with better composition, more accurate prompt adherence, and more natural lighting than Schnell at 4 steps. The generation takes longer, sure. We're talking maybe 45-60 seconds versus 10-15 seconds. But the quality difference is visible and consistent.

The exception is when speed matters more than quality. If you're doing rapid prototyping, testing compositions, or iterating on prompts, Schnell wins every time. I use Schnell for prototyping and then switch to Q5 Dev for final generations. That workflow has served me well for months.

If you're looking at smaller FLUX variants for your 8GB card, also check out my FLUX 2 Klein consumer GPU guide which covers the newer compact models designed specifically for consumer hardware.

Troubleshooting Common GGUF Issues

I've helped dozens of people get GGUF FLUX running in various communities and on Apatero.com, and the same problems come up over and over. Here are the most common issues and their fixes.

"CUDA out of memory" Errors

This is the big one. Even with quantization, you can still hit VRAM limits. Solutions in order of effectiveness:

1. Close all other GPU-accelerated applications (browsers, Discord, games)
2. Use --lowvram launch flag
3. Enable tiled VAE decoding
4. Drop to a lower quantization level (Q5 to Q4)
5. Reduce generation resolution to 768x768 or 512x512
6. On Windows, disable hardware GPU scheduling in Settings > Display > Graphics

If you're still hitting OOM after all of these, your card might have less usable VRAM than advertised. Some 8GB cards only have around 7.5GB actually available after the OS takes its share.

Black or Corrupted Images

This usually means the CLIP or VAE model is wrong or corrupted. The most common cause is using the wrong T5-XXL model. You need the fp8 version specifically. The full-precision T5-XXL will either crash your system or produce garbage output on an 8GB card. Re-download t5xxl_fp8_e4m3fn.safetensors and try again.

Extremely Slow Generation

If generation is taking 10+ minutes per image, you've likely triggered CPU offloading without realizing it. Check that ComfyUI is actually using your GPU by watching GPU utilization in Task Manager (Windows) or nvidia-smi (Linux). If GPU usage is near zero, the model is running on CPU. This usually happens when the model doesn't fully fit in VRAM. Try a lower quantization level or the --lowvram flag instead of --novram.

Node Not Found Errors

Make sure you installed the ComfyUI-GGUF extension properly and restarted ComfyUI. A surprising number of "it doesn't work" reports turn out to be people who installed the extension but forgot to restart. Also verify that the extension is in the right folder: ComfyUI/custom_nodes/ComfyUI-GGUF/, not nested inside another folder.

LoRA Not Applying

When using LoRAs with GGUF models, you need to use the standard LoRA loader node, not a special GGUF-specific one. The GGUF format handles the base model loading, but LoRAs are applied normally on top. Make sure your LoRA is connected between the UnetLoaderGGUF output and the KSampler input.

Real-World Performance Benchmarks

I ran all of these benchmarks on two cards: an RTX 4060 8GB and an RTX 3060 12GB, both running ComfyUI on Windows 11 with the latest NVIDIA drivers as of March 2026. These numbers give you a realistic picture of what to expect.

RTX 4060 8GB Performance

RTX 3060 12GB Performance

The RTX 4060 is faster per step thanks to its newer architecture, but the RTX 3060's extra VRAM gives it more flexibility with higher quantization levels and resolutions. If you're buying a card specifically for FLUX, that extra VRAM is worth more than the architectural improvements in most AI workloads. That's a point I cover in more detail in my best GPU for AI breakdown.

Generation time comparison across quantization levels. Lower is better. The sweet spot for 8GB cards is clearly Q4 or Q5 depending on your quality needs.

Advanced Tips for Getting the Most Out of GGUF FLUX

After months of daily use, I've picked up several techniques that aren't obvious from the documentation. These are the tricks that separate "it works" from "it works well."

Sampling Settings Matter More with Quantization

Quantized models are slightly more sensitive to sampler and scheduler choices than full-precision models. Through extensive testing, I've found that the euler sampler with the simple scheduler tends to be the most forgiving with quantized FLUX. The dpmpp_2m sampler also works well. Avoid dpm_fast and uni_pc with quantized models, as they tend to amplify any precision artifacts.

CFG scale is another area where quantization changes the game. Full-precision FLUX typically works best at CFG 1.0 (since FLUX uses guidance embedding rather than traditional CFG). With quantized models, I've found that bumping the guidance value slightly, to around 3.0-3.5, can help compensate for some of the lost precision. This is subtle, but on prompts that require fine detail, it makes a noticeable difference.

Batch Processing Strategy

Don't try to run batches on an 8GB card with FLUX GGUF. Generate one image at a time. If you need multiple variations, queue them sequentially rather than batching. Batching multiplies VRAM usage in ways that will crash your system. I use ComfyUI's queue system to line up 10-20 generations and let them run overnight. It's slower than batching, but it actually works, which is a significant advantage over not working at all.

Model Caching Behavior

ComfyUI keeps the loaded model in VRAM between generations. This means your second generation is faster than your first, because the model doesn't need to be reloaded. But it also means switching between models (say, from FLUX GGUF to SDXL and back) incurs a loading penalty each time. If you're doing a session with FLUX, stick with FLUX for the whole session. Plan your workflow to minimize model switches.

Combining with FLUX Schnell for Prototyping

Here's my personal workflow that balances speed and quality. I keep both FLUX Schnell (which runs fine on 8GB at full precision because it's designed for fast inference) and FLUX Dev GGUF Q5 available in my ComfyUI setup. I prototype compositions and prompt ideas with Schnell at 4 steps, which takes about 10 seconds. Once I'm happy with the composition, I switch to the Q5 Dev GGUF and generate the final image at 20-25 steps. This saves an enormous amount of time compared to iterating with the Dev model directly.

The team at Apatero.com uses a similar hybrid workflow for content creation, and it has cut our image generation time by roughly 60% compared to using Dev for everything.

What's Next for GGUF and FLUX Quantization?

The quantization landscape is evolving rapidly. A few developments worth watching:

The GGUF format itself continues to improve, with newer quantization methods offering better quality at the same bit depth. The community is actively working on FLUX-specific optimizations that take advantage of the model's architecture. We're likely to see purpose-built quantization schemes that outperform the generic methods currently in use.

NF4 (4-bit NormalFloat) quantization is another format gaining traction, supported natively in some ComfyUI nodes. It's not quite as flexible as GGUF, but it can produce slightly better quality at the 4-bit level. Keep an eye on this as an alternative to Q4 GGUF.

There's also ongoing work on dynamic quantization that adjusts precision per-layer based on each layer's sensitivity. This could eventually give us Q4-level VRAM usage with Q8-level quality, which would be a game-changer for 8GB card users.

For now, GGUF remains the most mature, best-supported, and most widely tested quantization format for FLUX in ComfyUI. If you're starting out with quantization today, GGUF is the right choice.

Frequently Asked Questions

Is GGUF quantization the same as fp8 quantization?

No, they're different approaches. GGUF uses integer quantization with sophisticated grouping and scaling techniques, while fp8 uses 8-bit floating point representation. GGUF offers more granular control over the compression level (Q4, Q5, Q6, Q8) while fp8 is a fixed 8-bit format. In practice, GGUF Q8 and fp8 produce very similar results, but GGUF's lower quantization levels (Q4, Q5) have no fp8 equivalent, making GGUF the better choice for 8GB VRAM cards.

Can I use GGUF FLUX models in Automatic1111 or Forge?

As of early 2026, GGUF support is primarily a ComfyUI feature through the city96 extension. Forge has experimental GGUF support through community extensions, but it's not as stable or well-tested as the ComfyUI implementation. Automatic1111 does not natively support GGUF models. If you want reliable GGUF FLUX, ComfyUI is the way to go.

Does quantization affect prompt adherence?

Slightly, yes. Lower quantization levels (Q4) can be less precise in following complex prompts with many specific details. If your prompt specifies "a woman wearing a blue dress with gold embroidery standing in front of a red brick wall at sunset," the Q4 model might get most of it right but miss the gold embroidery detail or simplify the brick texture. Q5 handles this much better, and Q8 is essentially identical to full precision in prompt adherence.

How do I know if my GPU has enough VRAM for a specific quantization level?

Run nvidia-smi in your terminal to see your current VRAM usage. Subtract your current usage from your total VRAM. The remaining amount needs to exceed the peak VRAM usage listed in the comparison table above by at least 500MB to account for overhead. For example, if you have an 8GB card with 400MB already in use, you have 7.6GB available, which is enough for Q5_K_S but tight.

Can I quantize FLUX models myself, or do I need to download pre-quantized versions?

You can quantize models yourself using tools like llama.cpp's quantization utilities adapted for diffusion models, but it's much easier to download pre-quantized versions from HuggingFace. city96 maintains well-tested quantized versions of all major FLUX models. Unless you have a specific reason to quantize yourself (like quantizing a fine-tuned model), use the pre-made ones.

Do GGUF models work with ControlNet?

Yes, GGUF FLUX models work with ControlNet preprocessors and apply nodes in ComfyUI. The ControlNet models themselves aren't affected by the base model's quantization. However, keep in mind that ControlNet models add to your VRAM usage, so on an 8GB card with Q5 FLUX, you might not have enough headroom for both a ControlNet model and a LoRA simultaneously. Plan your workflow accordingly.

Is there quality loss when using GGUF with FLUX.1 Schnell?

Schnell is already a distilled model, so quantizing it with GGUF compounds the quality loss from distillation with the loss from quantization. For this reason, I generally don't recommend GGUF quantization for Schnell unless you absolutely need it. Schnell is already designed to be lightweight, and its full-precision version runs on 8GB cards at lower resolutions. If you need to quantize, do it to FLUX.1 Dev where the quality headroom is much higher.

What's the difference between Q4_K_S and Q4_K_M?

The "_S" and "_M" suffixes refer to the quantization group size. "_S" (small) uses smaller groups, resulting in slightly lower memory usage but marginally lower quality. "_M" (medium) uses larger groups for slightly better quality at the cost of somewhat higher memory usage. On 8GB cards, the difference is meaningful. Q4_K_S might use 6.2GB where Q4_K_M uses 6.8GB. For tight VRAM budgets, stick with _S variants.

Can I use GGUF FLUX on AMD GPUs?

ComfyUI supports AMD GPUs through ROCm on Linux, and the GGUF extension works with this setup. However, AMD GPU support is less tested and you may encounter more issues compared to NVIDIA. The performance characteristics are also different, as AMD cards tend to have more VRAM for the price but slower per-step performance. If you have an AMD card with 8GB VRAM, the same quantization recommendations apply, but expect generation times to be roughly 30-50% longer than equivalent NVIDIA hardware.

How often are new GGUF quantizations released when FLUX updates?

The community is generally fast at producing GGUF quantizations for new FLUX releases. When a new FLUX version drops, quantized versions typically appear on HuggingFace within a few days. city96 is particularly reliable for this. Follow the ComfyUI-GGUF GitHub repository for the latest updates and compatibility notes.

Wrapping Up

GGUF quantization has genuinely democratized access to FLUX models. A year ago, running FLUX required a $1000+ GPU with 24GB of VRAM. Today, you can get 95% of that quality on a $300 card with 8GB. That's a massive shift, and it's one that the broader AI image generation community hasn't fully appreciated yet.

My recommendation for most 8GB VRAM users is straightforward: download FLUX.1 Dev Q5_K_S, install city96's ComfyUI-GGUF extension, use the fp8 T5-XXL text encoder, enable tiled VAE decoding, and start generating. You'll be producing high-quality FLUX images within 30 minutes of reading this guide.

If you're exploring FLUX for the first time and want to see what kind of results are possible with these quantized models combined with community LoRAs, check out our ultra real FLUX LoRAs collection. The quality achievable on consumer hardware today is genuinely impressive, and Apatero.com will continue covering the latest optimization techniques as they develop.

The 8GB VRAM barrier is no longer a barrier. It's just a different path to the same destination.