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README.md

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+---
+tags:
+- benchmark
+- amd
+- rocm
+- gemma4
+- local-llm
+- linux
+- spanish
+- latam
+language:
+- es
+- en
+license: apache-2.0
+---
+
+# Gemma 4 on AMD RX 6700 XT + ROCm
+
+> 🇲🇽 [Versión en Español](#versión-en-español) | 🇺🇸 [English Version](#english-version)
+
+Benchmarks of Google's Gemma 4 (April 2026) running on AMD GPU with ROCm on Linux.  
+**This documentation doesn't exist in Spanish — for the LatAm community.**
+
+---
+
+## English Version
+
+### Why this matters
+
+Most local AI guides assume:
+- NVIDIA GPU (CUDA)
+- Apple Silicon (macOS)
+- $1,500+ USD budget
+
+This repo documents that a **budget AMD GPU on Linux** running Gemma 4 — released April 2026 — outperforms a brand new Mac Mini M4 in decode speed, at a fraction of the cost.
+
+### Hardware
+
+| Component | Detail |
+|-----------|--------|
+| GPU | AMD RX 6700 XT 12GB (gfx1031) |
+| CPU | AMD Ryzen 5 5600G |
+| RAM | 16GB |
+| OS | Pop!_OS 24.04 LTS |
+| Ollama | 0.20.2 |
+| ROCm override | HSA_OVERRIDE_GFX_VERSION=10.3.0 |
+
+### Results (average of 3 runs)
+
+| Model | Prefill (tok/s) | Decode (tok/s) | VRAM | Status |
+|-------|----------------|----------------|------|--------|
+| gemma4:e2b | 1022.39 | 85.00 | ~2GB | ROCm ✅ full GPU |
+| gemma4:e4b | 697.91 | 57.34 | ~4GB | ROCm ✅ full GPU |
+| gemma4:26b | 157.94 | 10.70 | 12GB + RAM offload | ROCm ⚠️ partial |
+
+> **Note:** Prefill varies between runs due to KV cache warming.  
+> Decode is the number that matters for user experience — very consistent.
+
+### Platform comparison (decode ~4B models)
+
+| Hardware | Decode tok/s | Price (approx USD) |
+|----------|--------------|--------------------|
+| **RX 6700 XT + ROCm (this repo)** | **57-85** | existing hardware |
+| Mac Mini M4 16GB | 25-40 | ~$600 new |
+| RTX 3070 12GB | 50-80 | ~$500 used |
+
+**The RX 6700 XT doubles the Mac Mini M4 16GB in decode speed for models that fit in VRAM.**
+
+### The gfx1031 problem
+
+The RX 6700 XT reports `gfx1031` architecture, but ROCm doesn't officially support it.  
+Without the override, Ollama falls back to CPU (~3-5 tok/s).
+
+**Fix:**
+```bash
+sudo mkdir -p /etc/systemd/system/ollama.service.d
+
+sudo tee /etc/systemd/system/ollama.service.d/rocm-fix.conf << 'CONF'
+[Service]
+Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
+Environment="ROCR_VISIBLE_DEVICES=0"
+CONF
+
+sudo systemctl daemon-reload
+sudo systemctl restart ollama
+```
+
+**Verify it worked:**
+```bash
+journalctl -u ollama -n 10 --no-pager | grep "AMD Radeon"
+# Expected output:
+# description="AMD Radeon RX 6700 XT" total="12.0 GiB" available="11.1 GiB"
+```
+
+### Full setup
+
+```bash
+# 1. Install Ollama
+curl -fsSL https://ollama.com/install.sh | sh
+
+# 2. Apply ROCm fix (see above)
+
+# 3. Pull models
+ollama pull gemma4:e2b   # 2B - fast, fits easily
+ollama pull gemma4:e4b   # 4B - great quality, fits in VRAM
+ollama pull gemma4:26b   # 26B - requires RAM offload on 12GB
+
+# 4. Run
+ollama run gemma4:e4b "Hello, what is MLOps?"
+```
+
+### Run the benchmark yourself
+
+```bash
+chmod +x bench_gemma4.sh
+./bench_gemma4.sh
+```
+
+Runs 3 iterations per model, saves results to `gemma4_benchmark_results.md`.
+
+---
+
+## Versión en Español
+
+### Por qué esto importa
+
+La mayoría de guías de AI local asumen:
+- GPU NVIDIA (CUDA)
+- Apple Silicon (macOS)
+- Presupuesto de $1,500 USD o más
+
+Este repo documenta que una **GPU AMD de segunda mano en Linux** corriendo Gemma 4 (lanzada en abril 2026) supera en velocidad a un Mac Mini M4 nuevo, a una fracción del costo.
+
+### Hardware
+
+| Componente | Detalle |
+|------------|---------|
+| GPU | AMD RX 6700 XT 12GB (gfx1031) |
+| CPU | AMD Ryzen 5 5600G |
+| RAM | 16GB |
+| OS | Pop!_OS 24.04 LTS |
+| Ollama | 0.20.2 |
+| Override ROCm | HSA_OVERRIDE_GFX_VERSION=10.3.0 |
+
+### Resultados (promedio de 3 corridas)
+
+| Modelo | Prefill (tok/s) | Decode (tok/s) | VRAM | Estado |
+|--------|----------------|----------------|------|--------|
+| gemma4:e2b | 1022.39 | 85.00 | ~2GB | ROCm ✅ GPU completa |
+| gemma4:e4b | 697.91 | 57.34 | ~4GB | ROCm ✅ GPU completa |
+| gemma4:26b | 157.94 | 10.70 | 12GB + offload a RAM | ROCm ⚠️ parcial |
+
+> **Nota:** El prefill varía entre corridas por el KV cache warming.  
+> El decode es el número relevante para experiencia de usuario y es muy consistente.
+
+### Comparativa vs otras plataformas (decode modelos ~4B)
+
+| Hardware | Decode tok/s | Precio aprox MXN |
+|----------|-------------|-----------------|
+| **RX 6700 XT + ROCm (este repo)** | **57-85** | hardware existente |
+| Mac Mini M4 16GB | 25-40 | ~$23,000 MXN nuevo |
+| RTX 3070 12GB | 50-80 | ~$10,000 MXN usado |
+
+**La RX 6700 XT dobla en velocidad al Mac Mini M4 16GB en modelos que caben en VRAM.**
+
+### El problema del gfx1031
+
+La RX 6700 XT reporta arquitectura `gfx1031` pero ROCm no la tiene en su lista de soporte oficial.  
+Sin el override, Ollama cae a CPU (~3-5 tok/s).
+
+**Fix:**
+```bash
+sudo mkdir -p /etc/systemd/system/ollama.service.d
+
+sudo tee /etc/systemd/system/ollama.service.d/rocm-fix.conf << 'CONF'
+[Service]
+Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
+Environment="ROCR_VISIBLE_DEVICES=0"
+CONF
+
+sudo systemctl daemon-reload
+sudo systemctl restart ollama
+```
+
+**Verificar que funcionó:**
+```bash
+journalctl -u ollama -n 10 --no-pager | grep "AMD Radeon"
+# Output esperado:
+# description="AMD Radeon RX 6700 XT" total="12.0 GiB" available="11.1 GiB"
+```
+
+### Setup completo
+
+```bash
+# 1. Instalar Ollama
+curl -fsSL https://ollama.com/install.sh | sh
+
+# 2. Aplicar fix de ROCm (ver arriba)
+
+# 3. Descargar modelos
+ollama pull gemma4:e2b   # 2B - rápido, cabe fácil
+ollama pull gemma4:e4b   # 4B - buena calidad, cabe en VRAM
+ollama pull gemma4:26b   # 26B - requiere offload a RAM en 12GB
+
+# 4. Correr
+ollama run gemma4:e4b "Hola, explica qué es MLOps"
+```
+
+### Correr el benchmark tú mismo
+
+```bash
+chmod +x bench_gemma4.sh
+./bench_gemma4.sh
+```
+
+Corre 3 iteraciones por modelo y guarda los resultados en `gemma4_benchmark_results.md`.
+
+### Modelos probados
+
+| Modelo | Parámetros | Tipo | ¿Cabe en 12GB? |
+|--------|-----------|------|----------------|
+| gemma4:e2b | 2B | Dense | ✅ GPU completa |
+| gemma4:e4b | 4B | Dense | ✅ GPU completa |
+| gemma4:26b | 26B (4B activos, MoE) | MoE | ⚠️ offload parcial |
+
+---
+
+## About / Sobre este repo
+
+Made in CDMX with second-hand hardware and free software.  
+Hecho en CDMX con hardware de segunda mano y software libre.
+
+*[Positronica Labs](https://github.com/G10hdz) — Building AI tools for Latin America.*  
+*Hardware de segunda mano. Software libre. AI para todos.*