{
  "title": "AI GPU benchmarks comparison",
  "messages": [
    {
      "role": "user",
      "content": "Is there a site that compares performance for ai stuff on gpus?  Kind of like userbenchmark but for ai and computing "
    },
    {
      "role": "assistant",
      "content": "Yes — there *are* a few sites and benchmark suites that aim to compare GPU (and more broadly system) performance specifically for AI / ML / inference workloads (not just gaming). Which one is best depends on what you want to test (training, inference, large models, local vs cloud). Here are some good ones, plus pros/cons and what you should watch out for:\n\n---\n\n## ✅ Some Good Benchmark / Comparison Tools & Sites\n\n1. **AI-Benchmark (Desktop / Deep Learning Hardware Ranking)**  \n   They maintain rankings for desktop GPUs and CPUs under typical deep learning workloads. citeturn0search2  \n\n2. **Lambda GPU Benchmarks**  \n   Lambda (the ML hardware / cluster provider) publishes benchmark results for various GPUs running real ML workloads. citeturn0search0  \n\n3. **CUDO Compute — AI GPU Benchmarks**  \n   They compare things like fine-tuning time of LLMs, inference throughput, latency, etc. citeturn0search25  \n\n4. **MLPerf (especially MLPerf Client / Inference / Training)**  \n   This is more of a “standardized benchmark suite” rather than a direct comparison site, but many hardware vendors publish their MLPerf results, letting you compare across GPUs. citeturn0search6turn0search8  \n\n5. **TensorDock / “Machine Learning GPU Benchmarks”**  \n   Compares performance vs price across different GPUs for ML workloads. citeturn0search5  \n\n6. **Geekbench AI**  \n   This is newer — it tries to measure AI workloads across CPU, GPU, NPU using real ML test cases. citeturn0search16  \n\n7. **SkatterBencher AI Benchmark Leaderboard**  \n   Focused on overclocking / performance benchmarking for CPUs/GPUs under AI loads. citeturn0search4  \n\n8. **GPU Comparison Tool For AI (TheDataDaddi)**  \n   A simple tool to compare GPUs with metrics like FP16, FP32, price-performance, etc. citeturn0search18  \n\n---\n\n## ⚠️ Things to Watch Out For (Limitations & Caveats)\n\n- **Workload specificity**: A GPU might shine in some types of models (e.g. vision, diffusion, small LLM inference) but underperform for others (e.g. large LLM training, sparse attention). Benchmarks often pick a subset of model types.\n\n- **Precision / mixed precision / quantization**: Whether the test uses FP32, FP16, INT8, etc. can drastically change rankings. Some GPUs have hardware optimized for low-precision compute.\n\n- **Memory & memory bandwidth**: Some AI tasks are memory-bound. A GPU with more memory or higher bandwidth may outperform one with more raw compute in certain contexts.\n\n- **Thermal limits, power, and sustained performance**: A card might have burst performance but throttle under long loads.\n\n- **Software / driver / kernel / framework optimizations**: The same GPU can differ depending on software stack versions, CUDA / ROCm / TensorRT / PyTorch version, etc.\n\n- **Single vs multi-GPU scaling**: Some GPUs scale better across multiple cards or across NVLink / NVSwitch setups; others less so.\n\n- **Latency vs Throughput**: For inference, sometimes you care about latency (response time) rather than throughput (tokens/sec). Benchmarks often emphasize one or the other.\n\n---\n\nIf you like, I can point you to a “live” comparison dashboard (one that you can filter e.g. by model type, GPU, cost) and help you pick a benchmark suite to test *your* GPU. Want me to dig that for *your use case* (training vs inference, model types, local vs cloud)?"
    },
    {
      "role": "user",
      "content": "Yeah I like be able to compare one to the other. "
    },
    {
      "role": "assistant",
      "content": "Yes — there *are* a few sites / dashboards / benchmark suites that let you compare GPUs (or full systems) under AI / ML workloads. None are perfect (for reasons I’ll get to), but you can get useful relative comparisons. Below are some of the best ones + tips on how to interpret them, and a mini plan for how you could use them to pick or compare GPUs for *your* use case (inference, training, etc).\n\n---\n\n## 🔍 Good comparison / benchmark sites & tools\n\nHere are several good options where you can compare GPU / AI performance:\n\n| Name | What you can compare | Pros / caveats |\n|---|---|---|\n| Lambda GPU Benchmarks | Benchmarks of different GPUs on deep learning workloads (training & inference) on real models. citeturn0search0 | Useful, real workloads. But their configuration (batch sizes, model types) may not match your exact use case. |\n| TensorDock — Machine Learning GPU Benchmarks | Compares prices and performance across a dozen GPUs for ML workloads. citeturn0search2 | Good for getting a rough “bang for buck” comparison. |\n| AI Benchmark (desktop / deep learning ranking) | Ranking of GPUs / hardware for deep learning tasks. citeturn0search5 | Useful for broad comparison. |\n| MLPerf (Training / Client / Inference) | Standardized benchmark suite; vendors publish results. citeturn0search11turn0search20turn0search26 | Very good for “apples-to-apples” comparisons when configurations are consistent. But results depend heavily on software/hardware stack (drivers, CUDA versions, etc.). |\n| Epoch.ai Benchmarks | Database of benchmark results for AI models & tasks. citeturn0search13 | Good for seeing model-level performance. |\n| Geekbench AI | Cross-platform benchmark testing CPU, GPU, NPU on real ML tasks. citeturn0search28 | Nice for comparing more modest systems (laptops, desktops) and mixed hardware. |\n| “AI-ML GPU Benchmark Dashboard” (All About Data) | A dashboard for benchmarking local GPU/CPU for AI workloads. citeturn0search24 | Could be helpful to run your own tests and compare your system against others. |\n\n---\n\n## 🧭 How to use these (and pitfalls)\n\nComparisons are useful, but you have to watch for:\n\n- **Workload mismatch**: A GPU might be great for image models but weaker for transformers, or inference vs training.  \n- **Precision / quantization**: FP32 vs FP16 vs INT8 vs quantized models — GPU hardware supports these differently.  \n- **Memory & batch sizes**: If your model or data needs more VRAM than the GPU supports, performance suffers or might not even run.  \n- **Software / driver stack**: CUDA versions, cuDNN / ROCm, tensor core usage, framework versions all heavily affect results.  \n- **Thermal throttling / sustained loads**: Benchmarks often show bursts; real workloads over hours may behave differently.  \n- **Scalability / multi-GPU behavior**: Some GPUs scale better in parallel setups; some interconnects (NVLink, PCIe) matter a lot.\n\nSo when you see a “GPU A is 2× GPU B” result, ask: *on what models, with what precisions, with what batch sizes, with how many GPUs, with what software version?*\n\n---\n\n## 📋 Suggested plan (for *you*) to compare GPUs meaningfully\n\n1. **Define your target workload(s)**  \n   - Are you doing **training**, **fine-tuning**, **inference**, or a mix?  \n   - What model architectures (transformers, CNNs, diffusion, etc.)?  \n   - What precision (FP32, FP16, mixed, quantized)?  \n   - What data / batch sizes / memory requirements?\n\n2. **Pick a benchmark suite or tool**  \n   Use something like MLPerf (if your use case aligns), or run a custom benchmark using tools like the “AI-ML GPU Benchmark Dashboard” or your own code.\n\n3. **Run benchmarks on candidate GPUs (or systems)**  \n   Either pick GPUs you’re considering (or have) and run against the same benchmark.  \n   Alternatively, use published benchmarks from the sites above, but verify their configuration.\n\n4. **Normalize & compare**  \n   Adjust comparisons by cost, power draw, memory, sustained throughput.  \n   Create a table: GPU | throughput (your workload) | cost | performance per dollar | memory headroom | scalability.\n\n5. **Validate with a small “real run”**  \n   Once you pick a GPU (or a candidate), test it with a smaller version of your actual workload to see whether the benchmark projections hold in your environment.\n\n---\n\nIf you like, I can build you a “custom GPU comparison dashboard” (just a table or tool) for GPUs you care about (say RTX 4070, 4080, 4090, A100, etc) *for your target tasks* (training, inference) and send you a link / file. Do you want me to do that? If yes, tell me which GPUs you’re looking at and what tasks."
    },
    {
      "role": "user",
      "content": "What is interference "
    },
    {
      "role": "assistant",
      "content": "In the context of AI and GPUs, I think you meant **“inference”**, not *interference* — easy mix-up.\n\nHere’s the distinction:\n\n---\n\n### 🧠 **Training vs. Inference**\n\n| Stage | What Happens | GPU Usage Pattern | Example |\n|-------|---------------|------------------|----------|\n| **Training** | The model *learns* patterns by adjusting weights through millions of calculations. | Heavy compute, long sessions, high memory use. | Training a GPT model or fine-tuning Stable Diffusion. |\n| **Inference** | The model is *used* to make predictions or generate outputs using the trained weights. | Short bursts, faster runtime, often optimized with quantization or batching. | Typing a prompt into ChatGPT or generating an image with a pre-trained model. |\n\n---\n\n### ⚙️ **Think of it like this**\n\n- **Training** → Teaching the AI to drive by showing it tons of examples.  \n- **Inference** → Letting the AI actually drive the car using what it learned.\n\n---\n\n### 💡 Why it matters for GPU comparisons\n\nWhen comparing GPUs for AI workloads:\n- **Training benchmarks** stress *raw compute* (TFLOPs, memory bandwidth).\n- **Inference benchmarks** stress *throughput and latency* (how many tokens/images/sec).\n- Some GPUs are optimized for **inference** (e.g. NVIDIA L40S, A10, RTX 4070)  \n  while others are beasts for **training** (e.g. A100, H100, 4090, MI300).\n\n---\n\nWould you like me to show a side-by-side table of *training vs inference performance* for a few GPUs (like 4070, 4090, A100, H100)?"
    },
    {
      "role": "user",
      "content": "What about the MI60. What’s that good for?"
    },
    {
      "role": "assistant",
      "content": "Good question — the **AMD Radeon Instinct MI60** is an interesting GPU. It’s not new (released late 2018), but it still shows up in datacenter or budget-AI setups. Let’s break down what it *is*, what it’s good at, and where it struggles today:\n\n---\n\n## ⚙️ Overview: AMD Radeon Instinct MI60\n\n| Spec | Detail |\n|------|--------|\n| Architecture | **Vega 20** (7 nm) |\n| Compute Units | 64 CUs / 4096 Stream Processors |\n| Base / Boost | 1200 MHz / 1800 MHz typ. |\n| Memory | **32 GB HBM2**, 4096-bit bus, ~1 TB/s bandwidth |\n| Interface | PCIe 4.0 ×16 / Infinity Fabric Link |\n| FP64 (64-bit) | 7.4 TFLOPs |\n| FP32 (32-bit) | 14.8 TFLOPs |\n| FP16 (Half-precision) | **29.5 TFLOPs** (peak) |\n| Power | 300 W TDP |\n\n---\n\n## 🧠 What It’s *Good For*\n\n### 1. **Scientific / HPC workloads**\n- Strong **FP64 (double-precision)** makes it solid for **numerical simulation, finite-element analysis, physics, chemistry**, etc.  \n- Competes roughly with NVIDIA’s V100 in FP64.\n\n### 2. **Large-memory inference or mixed-precision compute**\n- 32 GB HBM2 is great for **batch inference**, or running **larger models** (LLMs, diffusion) if you stay in FP16/FP32.\n- High bandwidth (1 TB/s) helps when the workload is **memory-bound**.\n\n### 3. **Open-source / ROCm-based environments**\n- Works with **PyTorch ROCm**, **TensorFlow ROCm**, **ONNX Runtime**, and **Blender HIP** rendering.\n- Best suited for Linux systems where you can use **ROCm 6.x** drivers.\n\n---\n\n## ⚠️ Weak Spots\n\n| Limitation | Impact |\n|-------------|--------|\n| 🧩 **No Tensor Cores / INT8 acceleration** | Slower at modern transformer or quantized inference compared to NVIDIA Turing/Ampere. |\n| 🔌 **Driver / ROCm finickiness** | You need recent ROCm versions and kernel support; Windows support is minimal. |\n| 🧠 **Older architecture** | No sparsity or mixed-precision hardware acceleration; higher power per TFLOP. |\n| 💬 **Community support smaller** | Tutorials and prebuilt Docker images are mostly NVIDIA-oriented. |\n| 💰 **Used-market only** | Typically $250–400 USD on eBay; can be cost-effective but not plug-and-play. |\n\n---\n\n## 🧮 Practical Translation\n\n| Use Case | MI60 Viability |\n|-----------|----------------|\n| **LLM inference (7B-13B models)** | ✅ Possible under ROCm; expect 20–25 tokens/s for 7B if tuned. |\n| **LLM training (small models)** | ⚠️ Feasible but much slower than A100/4090; driver tuning required. |\n| **HPC science / simulation** | ✅ Excellent (FP64). |\n| **Diffusion / image gen** | ⚠️ Works, but slower than RTX 30-/40-series. |\n| **Gaming / Windows workloads** | 🚫 Poor driver support; not intended. |\n\n---\n\n## 🧩 Bottom Line\n\nThe **MI60** is basically a **server-grade Vega** designed for **scientific compute and heavy FP64/FP32 workloads**, not modern tensor-core-optimized AI training.  \nIf you already have one (or can get it cheap), it’s **fine for hobbyist ML** or **open-source ROCm experimentation**, but for serious AI training or fast inference, a **4090, A100, or MI300** will demolish it in efficiency.\n\n---\n\nWant me to show how an MI60 stacks up against a 4090 or A100 on a few real-world benchmarks (tokens/s for Llama 2, diffusion steps/s, etc.)?"
    },
    {
      "role": "user",
      "content": "Do you know if you can run HRC hold resource calculator on a gpu?"
    },
    {
      "role": "assistant",
      "content": "I did some digging — I couldn’t find any evidence that **Hold’em Resources Calculator (HRC)** supports GPU acceleration (using CUDA, ROCm, etc.). The documentation and descriptions all suggest it’s a CPU / memory-based solver. citeturn0search2turn0search5turn0search0\n\nHere’s how I reached that conclusion — and some ideas in case you want to try pushing it to GPU:\n\n---\n\n## 🔍 What the evidence says\n\n- The HRC “Documentation” page describes things like Monte Carlo sampling, memory configuration, “optimizing memory usage” etc. but **no mention of GPU** or hardware acceleration. citeturn0search2  \n- The product descriptions focus on CPU, RAM, tree size, abstraction settings, and so forth. citeturn0search5turn0search0  \n- A forum thread mentions licensing and machine usage but doesn’t suggest GPU use. citeturn0search1  \n- The software is presented as a solver for “preflop, postflop, ICM,” etc. — typical workloads in HRC tend to be combinatorial and memory/computation heavy in the CPU domain. citeturn0search5turn0search0  \n\nSo unless there's a hidden / experimental GPU branch or plugin, it seems HRC is not designed to run on GPU natively.\n\n---\n\n## 💡 Possibilities & workarounds\n\nEven if HRC doesn’t natively support GPU, you might explore:\n\n1. **Request or check with the developers**  \n   Ask HRC’s support whether a GPU-accelerated version is planned, or if they have an API or plugin that uses GPU.\n\n2. **Hybrid / Offload parts**  \n   If HRC’s workload has clear kernels (e.g. Monte Carlo sampling, large matrix operations) that *can* run on GPU, one could in theory reimplement or wrap them in GPU code and feed results back. But that’s nontrivial and requires reverse engineering or cooperation from HRC.\n\n3. **Parallel / distributed CPU scaling**  \n   Use multiple CPU cores, or distribute across machines, since solver tasks may parallelize. That might give most of the speed benefits you’d hope for from GPU.\n\n4. **Alternative solvers that *do* support GPU**  \n   Look if there’s an alternative solver or open-source version of HRC logic that supports GPU acceleration (e.g. custom poker solvers written in PyTorch/CUDA or a GPU-accelerated game-theory library).\n\n---\n\nIf you like, I can try to find if there’s any GPU-accelerated poker / game-theory solvers (HRC alternatives) and see whether someone has already tried hacking HRC to use GPU. Want me to check that?"
    }
  ]
}