RX 7000ConsumerRDNA 3PCIe 4ROCm
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 7800 XT 16GB is a capable RDNA 3 mid-range GPU with 16 GB of GDDR6 VRAM — more than most NVIDIA cards at a similar price. It can run 13B models at FP16 and larger models with quantization. Community ROCm support makes it usable for llama.cpp and some ML frameworks with configuration, though it is not on AMD's official ROCm support list. It represents a strong VRAM-per-dollar option if you're willing to deal with community-level ROCm setup.
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) |
rocm-experimentalhigh-vramgood-vram-per-dollarsoftware-limited
Specifications
Compute
FP1639 TFLOPS
INT8312 TOPS
ArchitectureRDNA 3
Memory
VRAM16 GB
Bandwidth624 GB/s
TypeGDDR6
General
FamilyRX 7000
SegmentConsumer
InterconnectPCIe 4
Compute PlatformROCM
MSRP$499
TDP263W
Key Features
RDNA 3 architecture (Navi 32 die)16 GB GDDR6 on a 256-bit bus624 GB/s memory bandwidth60 Compute UnitsAMD Infinity Cache (second-generation, 64 MB)Community ROCm support — not officially listed
For AI Workloads
Strengths
- 16 GB VRAM at mid-range pricing is difficult to match from NVIDIA
- 624 GB/s bandwidth is among the better RDNA 3 mid-range specs
- Community ROCm support is functional and improving for RDNA 3
- llama.cpp Vulkan works as a reliable no-setup fallback
Considerations
- Not on AMD's official ROCm support list — manual configuration required
- High TDP (263W) for its tier reduces efficiency
- PyTorch and TensorFlow ROCm support requires additional setup and may be unstable
- RTX 4070 12GB has better software support with slightly less VRAM
RDNA 3 is AMD's chiplet-based GPU architecture, combining a 5nm Graphics Compute Die (GCD) with 6nm Memory Cache Dies (MCDs). It introduces AI accelerators and a new unified compute unit design.
AI Relevance
ROCm support for RDNA 3 is maturing but lags behind NVIDIA's CUDA ecosystem. AI accelerator units provide some inference acceleration, but lack the dedicated Tensor Core equivalent found in NVIDIA GPUs.
Process: TSMC 5nm + 6nmPlatform: ROCMPrecisions: FP32, FP16, BF16, INT8
Recommendations by Workload
Qwen 3.5 9B 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 59.2 tok/s · 45K ctx · llama.cppEST.
Qwen 3.5 9B is a specialized fit for 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 75.8 tok/s · 58K 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 ~21.8 GB
397BTier 100Needs ~246.1 GB
123BTier 100Needs ~80.2 GB
1000BTier 100Needs ~616.2 GB
1000BTier 100Needs ~616.2 GB
Image & Video Generation
Diffusion Model Compatibility
31 of 52 models can generate images or video on your RX 7800 XT 16GB
Upgrade paths
Upgrade from RX 7800 XT 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
RX 7800 XT 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~10.3s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~46.4s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~2m 33s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~9s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~26.4s/frame |
Qwen 3.5 9B 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 75.8 tok/s · 58K ctx · llama.cppEST.
Qwen 3.5 9B matches Reasoning 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 59.2 tok/s · 45K ctx · llama.cppEST.
Granite 4.1 8B 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 85.2 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
49 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~24.2s/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.
Buying advice
Should you buy RX 7800 XT 16GB for local AI?
Usable for local AI with limits
Can run 11 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.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 2 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.
