RX 500ConsumerGCN 4PCIe 3ROCm
Operating mode
Choose the operating mode for this hardware
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The RX 580 8GB is a GCN 4 (Polaris) GPU from 2017, originally designed for gaming. Modern AI inference frameworks do not support GCN 4 in any meaningful way — no ROCm, no practical Vulkan compute support. Basic llama.cpp Vulkan may function on some Linux configurations, but performance is extremely slow at 6 TFLOPS FP16. This GPU is not recommended for any AI use case.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
no-rocmlegacynot-recommended-for-ai
Specifications
Compute
FP166 TFLOPS
INT812 TOPS
ArchitectureGCN 4
Memory
VRAM8 GB
Bandwidth256 GB/s
General
FamilyRX 500
SegmentConsumer
InterconnectPCIe 3
Compute PlatformROCM
MSRP$229
Key Features
GCN 4 architecture (Polaris 20)8 GB GDDR5 on a 256-bit bus256 GB/s memory bandwidth36 Compute UnitsPCIe Gen 3 x16No ROCm support — architecture is too old
For AI Workloads
Strengths
- 8 GB VRAM is the only notable upside
- Widely available for very low cost on the used market
Considerations
- No ROCm support — GCN 4 is not in AMD's ROCm compatibility list
- Vulkan compute support is limited and unreliable for inference
- 6 TFLOPS FP16 makes inference impractically slow even for small models
- Not recommended for AI — any RDNA 3 card is massively superior
GCN 4 (Graphics Core Next 4th gen) is AMD's 14nm refresh of the Polaris architecture, powering the RX 500 series. It was the mainstream competitor to NVIDIA's Pascal GTX 10 series.
AI Relevance
No practical AI inference capability. Lacks the compute precision and memory bandwidth needed for LLM workloads. Only usable for very small models via CPU offloading with Vulkan backend.
Process: GlobalFoundries 14nmPlatform: ROCMPrecisions: FP32, FP16
Recommendations by Workload
Qwen 3.5 4B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 48.5 tok/s · 28K ctx · llama.cppEST.
Codestral Mamba 7B is a specialized fit for Coding. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 29.6 tok/s · 67K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.0 GB
397BTier 100Needs ~245.3 GB
123BTier 100Needs ~79.4 GB
1000BTier 100Needs ~615.4 GB
1000BTier 100Needs ~615.4 GB
Image & Video Generation
Diffusion Model Compatibility
21 of 52 models can generate images or video on your RX 580 8GB
Upgrade paths
Upgrade from RX 580 8GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
RX 580 8GBCategory AvgRTX 3080 10GB
| Image Gen (SDXL) | Runs with sequential offload | SDXL 1.0 FP16 | ~~4m 16s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~7m 15s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~8m 51s per image |
| Video Short (25f) | Won't fit | LTX Video 2B | ~~1m 24s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~4m 7s/frame |
Gemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 29.2 tok/s · 96K ctx · llama.cppEST.
Codestral Mamba 7B is viable for Reasoning, but is not the most specialized choice. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 29.6 tok/s · 67K ctx · llama.cppEST.
Granite 4.1 3B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 42.0 tok/s · 59K ctx · llama.cppEST.
Image
| MAGI-1Video | 256×256 | ~3m 47s/frame | F |
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
There are 4 upgrade path(s) from RX 580 8GB: RTX 3080 10GB, RX 7700 XT 12GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy RX 580 8GB for local AI?
Usable for local AI with limits
Can run 7 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
What will limit you first
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best upgrade itinerary
Unlocks 33 additional models that do not fit on the current setup.
Want more headroom? RTX 3080 10GB (10.0 GB VRAM) is the next step up.