~/src/llama.cpp/build$ bin/llama-cli -ngl 15 -m /opt/ai/mistral/Ministral-3-8B-Instruct-2512-Q4_K_M.gguf -c 4096 --temp 0.15
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce GTX 1650 SUPER, compute capability 7.5, VMM: yes
Loading model...
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build : b7372-482211438
model : Ministral-3-8B-Instruct-2512-Q4_K_M.gguf
modalities : text
available commands:
/exit or Ctrl+C stop or exit
/regen regenerate the last response
/clear clear the chat history
/read add a text file
> What are the most recent consumer GPUs you know about? How they perform in AI applications?
As of my last knowledge update in **October 2023**, here are some of the most recent and high-performance **consumer-grade GPUs** (as of early 2024) and their relevance to **AI applications**, particularly for tasks like deep learning, inference, and training:
---
### **1. NVIDIA GPUs (Dominant in AI)**
NVIDIA continues to lead in AI acceleration with its **Hopper (H100)** architecture (primarily in data center) and **Ada Lovelace (RTX 40-series)** for consumer/enthusiast GPUs.
#### **Latest Consumer GPUs (2023–2024):**
- **RTX 4090**
- **Architecture**: Ada Lovelace (4th-gen DLSS, improved Tensor Cores).
- **Key Features**:
- **24GB GDDR6X** (huge for AI workloads).
- **76B Tensor Cores** (for mixed-precision AI training/inference).
- **DLSS 3** (AI upscaling, useful for generative AI pipelines).
- **PCIe 5.0** (faster data transfer).
- **AI Performance**:
- **Blazing fast for inference** (e.g., LLMs, transformers).
- Supports **FP8/FP16/BF16** for efficient training.
- **NVENC/H.266** for AI-generated video.
- **CUDA Cores**: 16,384 (great for parallel workloads).
- **Use Cases**: LLM fine-tuning, real-time AI rendering, generative AI (Stable Diffusion, etc.).
- **RTX 4080 Super / RTX 4080**
- **Architecture**: Ada Lovelace (slightly less VRAM than 4090).
- **Key Features**:
- **16GB/12GB GDDR6X** (still strong for AI).
- **DLSS 3**, **Tensor Cores**, and **NVENC**.
- **AI Performance**:
- Good for **inference** (e.g., running LLMs like Llama 2 on local machines).
- Slightly slower than 4090 in training due to less VRAM.
- **RTX 4070 Ti Super**
- **Architecture**: Ada Lovelace.
- **Key Features**:
- **16GB GDDR6X**.
- **DLSS 3**, **Tensor Cores**, but fewer CUDA cores (~7,168).
- **AI Performance**:
- Decent for **lightweight AI tasks** (e.g., running smaller models like Mistral-7B with quantization).
- **RTX 4070**
- **Architecture**: Ada Lovelace.
- **Key Features**:
- **12GB GDDR6X**.
- Still supports **DLSS 3** and **Tensor Cores**.
- **AI Performance**:
- Limited by VRAM for large models (e.g., Llama 70B may require offloading).
#### **NVIDIA’s AI-Specific GPUs (Data Center):**
While not consumer-grade, these are relevant for AI workloads:
- **NVIDIA H100** (Hopper, **80GB HBM3**):
- **Best for AI training** (FP8 support, **3x faster than A100** in some cases).
- Used in **AI supercomputers** (e.g., NVIDIA GH200).
- **L40/L40S** (for enterprise AI inference).
---
### **2. AMD GPUs (Growing in AI Support)**
AMD’s **RDNA 3** and **CDNA 3** architectures are improving, but NVIDIA still dominates in AI acceleration.
#### **Latest Consumer GPUs (2023–2024):**
- **RX 7900 XTX / RX 7900 XT**
- **Architecture**: RDNA 3.
- **Key Features**:
- **24GB/20GB GDDR6**.
- **FSR 3** (AMD’s alternative to DLSS).
- **No native Tensor Cores** (but supports **ROCm** for AI).
- **AI Performance**:
- **Not optimized for AI** like NVIDIA (no dedicated Tensor Cores).
- Can run **ROCm-based AI frameworks** (e.g., PyTorch, TensorFlow on Linux).
- Better for **general computing** than pure AI.
- **RX 7800 XT**
- **Architecture**: RDNA 3.
- **Key Features**:
- **16GB GDDR6**.
- **FSR 3**, but weaker AI capabilities than NVIDIA.
#### **AMD’s AI GPUs (Data Center):**
- **MI300X** (CDNA 3, **128GB HBM3**):
- **Designed for AI training** (supports FP8, **10x faster than A100** in some benchmarks).
- Used in **AI cloud providers** (AWS, Azure).
---
### **3. Intel GPUs (Emerging in AI)**
Intel’s **Arc Alchemist** (GPU) and **Gaudi** (AI accelerator) are gaining traction.
#### **Latest Consumer GPU:**
- **Arc A770 / A750**
- **Architecture**: Alchemist.
- **Key Features**:
- **16GB GDDR6**.
- **XMX (Xe Matrix Extensions)** for AI (but **not as mature as NVIDIA’s Tensor Cores**).
- **No native AI acceleration** yet (early-stage support).
- **AI Performance**:
- **Not recommended for serious AI work** (yet).
- May improve with **oneAPI** and **ROCm** support.
#### **Intel’s AI Accelerators:**
- **Gaudi 2** (for data centers):
- **Specialized for AI training** (used by **Meta, Microsoft**).
- **No consumer version yet**.
---
### **Performance Comparison for AI Workloads**
| GPU | VRAM | Tensor Cores | AI Optimization | Best For |
|-------------------|-------|--------------|------------------|-----------------------------------|
| **RTX 4090** | 24GB | 76B | Excellent | LLM training/inference, generative AI |
| **RTX 4080 Super**| 16GB | 68B | Very Good | Inference, smaller models |
| **RTX 4070 Ti** | 16GB | 58B | Good | Lightweight AI tasks |
| **RX 7900 XTX** | 24GB | None | Poor (ROCm) | General computing, not AI |
| **Arc A770** | 16GB | XMX (early) | Limited | Not for AI (yet) |
| **NVIDIA H100** | 80GB | 160B | Best (FP8) | Enterprise AI training |
---
### **Key Takeaways for AI Applications**
1. **For AI Training/Inference**:
- **NVIDIA RTX 4090** is the **best consumer GPU** for AI (LLMs, Stable Diffusion, etc.).
- **RTX 4080 Super** is a good alternative if you need slightly less VRAM.
- **NVIDIA H100** (data center) is **unmatched** for large-scale training.
2. **For Inference (Running Models)**:
- **Tensor Cores** (NVIDIA) + **DLSS 3** (for AI-generated content) make RTX 40-series ideal.
- **FP16/BF16/FP8** support helps with efficiency.
3. **For General AI (Non-NVIDIA)**:
- **AMD RX 7900 XTX** can run **ROCm-based AI** but is **not optimized** like NVIDIA.
- **Intel Arc** is **not recommended** for AI yet.
4. **Future Trends**:
- **NVIDIA’s Blackwell (B100)** (expected late 2024) will push AI performance further.
- **AMD’s CDNA 4** and **Intel’s future GPUs** may close the gap.
---
### **Recommendations**
- **For serious AI work (training/inference)**: **RTX 4090** (if budget allows) or **RTX 4080 Super**.
- **For lightweight AI (inference only)**: **RTX 4070 Ti** or **RTX 4070**.
- **For general computing + some AI**: **RX 7900 XTX** (but expect slower AI performance).
- **For enterprise AI**: **NVIDIA H100** or **AMD MI300X**.
Would you like benchmarks for specific AI tasks (e.g., running Llama 2, Stable Diffusion)?
[ Prompt: 302.5 t/s | Generation: 9.9 t/s ]
