--- 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.*