At AIFinitee, we have spent months benchmarking LLM inference on our dual-node AMD Ryzen AI MAX 395+ cluster. We have measured tokens per second across MiniMax-M2 and Qwen3.5-397B. We have shown how Linux delivers 15-30% more performance than Windows.
But here is the question nobody in the local AI community is asking: what if raw speed is not the whole story?
This post compares AMD’s 96 GB unified memory architecture against NVIDIA’s 16 GB consumer GPUs for ComfyUI workloads. We will show you exactly what models fit, what does not, and why video generation might be the killer app for unified memory.
The Core Difference: Unified Memory vs Dedicated VRAM
| Architecture | AMD Ryzen AI MAX 395+ | NVIDIA RTX 4070 Ti (16 GB) |
|---|---|---|
| Memory Type | DDR5 system RAM (unified) | GDDR6 dedicated VRAM |
| Total Capacity | 96 GB (shared CPU+iGPU) | 16 GB (GPU only) |
| Memory Bandwidth | ~100-150 GB/s | ~450-670 GB/s |
| Primary Advantage | Capacity for large models | Raw bandwidth for speed |
The Tradeoff: AMD gives you 6x more memory at ~3x lower bandwidth. NVIDIA gives you faster iteration on models that fit, but a hard ceiling at 16 GB.
What Actually Fits: Model-by-Model Breakdown
Image Generation Models
| Model | VRAM Required | AMD 96 GB | NVIDIA 16 GB |
|---|---|---|---|
| Stable Diffusion 1.5 | 4-6 GB | Yes | Yes |
| SDXL 1.0 | 10-12 GB | Yes | Yes |
| SDXL Turbo | 8-10 GB | Yes | Yes |
| Flux.1 Dev | 20-24 GB | Yes | No (OOM) |
| Flux.1 Pro | 24-30 GB | Yes | No (OOM) |
| SDXL + 3x ControlNet | 14-16 GB | Yes | Borderline |
Video Generation Models
| Model | VRAM Required | AMD 96 GB | NVIDIA 16 GB |
|---|---|---|---|
| Wan 2.1 T2V | 40-50 GB | Yes | No (OOM) |
| Wan 2.2-T2V-A14B | 70-80 GB | Yes | No (OOM) |
| Stable Video Diffusion | 16-20 GB | Yes | May fit |
The Pattern: Image generation works on both (for SDXL). Video generation only works on AMD for consumer hardware.
Why Video Models Demand 80 GB: The Temporal Tax
Wan 2.2 is not just bigger – it is fundamentally different. Here is why:
1. Temporal Attention Layers
| Model | Tokens Attended | Memory Scaling |
|---|---|---|
| SDXL (image) | ~1,024 tokens | O(n) = 1M entries |
| Wan 2.2 (video) | ~122,880 tokens (120 frames) | O(n^2) = 15B entries |
Video models attend across time and space. That 120-frame sequence requires attention matrices that grow quadratically.
2. Multi-Frame Latent Buffers
- SDXL: Stores 1 latent (~64x64x4 floats)
- Wan 2.2: Stores 120+ latents simultaneously for temporal coherence
- KV cache must hold the entire sequence during autoregressive generation
3. Mixture of Experts Overhead
Wan 2.2-T2V-A14B specifications:
- Total Parameters: 27B
- Active Per Step: 14B (MoE routing)
- Expert Count: 2 experts
- Peak VRAM: ~80 GB with offloading disabled
4. Motion + Appearance Modeling
| Component | Image Model | Video Model |
|---|---|---|
| Spatial features | Yes | Yes |
| Temporal dynamics | No | Yes |
| Optical flow | No | Yes |
| Motion vectors | No | Yes |
Extra conditioning layers = extra memory that does not exist in image models.
The NVIDIA 16 GB Reality Check
For ComfyUI users on consumer NVIDIA hardware:
| Workflow | RTX 4070 Ti (16 GB) | AMD 96 GB UMA |
|---|---|---|
| SDXL base generation | Fast | Works |
| SDXL + ControlNet | Tight | Comfortable |
| Flux.1 Dev | No (OOM) | Native |
| Wan 2.2 T2V | Impossible | Native |
| Multi-stage pipelines | No (OOM) | Native |
The Verdict: NVIDIA wins on speed for SDXL. AMD wins on capability for anything larger, at the expense of speed..
When to Choose AMD vs NVIDIA
Choose AMD Ryzen AI MAX If:
| Use Case | Why AMD Wins |
|---|---|
| Video generation (Wan 2.2, SVD) | Only architecture that fits consumer hardware |
| Flux.1 Pro workflows | 24-30 GB requirement exceeds 16 GB |
| Multi-ControlNet pipelines | 14-16 GB+ workloads with headroom |
| Research experimentation | Try models without constant OOM errors |
| Budget constraints | ~$3K build vs $15K+ A100 cluster |
Choose NVIDIA If:
| Use Case | Why NVIDIA Wins |
|---|---|
| SDXL iteration speed | 3-4x faster generation |
| Production pipelines | TensorRT optimizations |
| Stable Diffusion 1.5 workloads | Mature CUDA ecosystem, less setup time |
| Time-sensitive work | Faster iteration = more experiments |
The Practical Verdict
| Metric | AMD 96 GB UMA | NVIDIA RTX 5070 Ti/ 5080 16 GB GDDR6 |
|---|---|---|
| Model compatibility | Runs everything | Hard 16 GB ceiling |
| SDXL speed | 15-25 sec/image | 5-8 sec/image |
| Flux.1 speed | 30-45 sec/image | 10-15 sec/image (if fits) |
| Wan 2.2 capability | 3-5 min/video | Run quantized versions (lower quality) |
| Multi-stage pipelines | Native execution | Requires unloading |
Our Recommendation:
If you are doing less intensive image-generation and speed matters, NVIDIA wins. But if you want to experiment with video generation, Flux, or complex pipelines, AMD’s 96 GB unified memory is the only consumer hardware that makes it possible, at the expense of speed.
The speed gap is real. But so is the capability gap. Sometimes running it once for quality output beats iterating five times on a model that does not fit.
Thanks for reading – see you in the next one.
Hardware Used: AMD Ryzen AI MAX 395+ (96 GB unified memory)
Software: ComfyUI v1.4.2, PyTorch 2.6.0+rocm6.2
Models Tested: SDXL 1.0, Flux.1 Dev, Wan 2.2-T2V-A14B
