Radeon RX 7000 MobileLaptopRDNA 3MOBILEROCm
Operating mode
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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 Radeon RX 7800M 12GB is a high-performance RDNA 3 mobile GPU intended for gaming laptops. The 12 GB of GDDR6 VRAM enables 7B FP16 and 13B Q4 models, making it a capable laptop AI inference option. Community ROCm support works on desktop RDNA 3 and may extend to this mobile variant with the gfx override approach on Linux. It is a solid dual-purpose gaming/AI laptop GPU.
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-experimentallaptopsoftware-limited
Specifications
Compute
FP1631 TFLOPS
INT8248 TOPS
ArchitectureRDNA 3
Memory
VRAM12 GB
Bandwidth432 GB/s
General
FamilyRadeon RX 7000 Mobile
SegmentLaptop
InterconnectMOBILE
Compute PlatformROCM
Key Features
RDNA 3 architecture (Navi 32 mobile)12 GB GDDR6 on a 192-bit bus432 GB/s memory bandwidth40 Compute UnitsMobile interconnectCommunity ROCm support via gfx1100 workaround
For AI Workloads
Strengths
- 12 GB VRAM covers 7B FP16 and 13B Q4 in a laptop package
- Community ROCm works on RDNA 3 Navi 32 die variants
- 432 GB/s mobile bandwidth is above average for laptops
- Good dual-purpose gaming and AI inference hardware
Considerations
- Not officially ROCm supported — requires manual environment variable setup
- Thermal and power envelope limits sustained AI workload performance
- Community ROCm support on mobile parts is less validated than desktop
- llama.cpp Vulkan is recommended over ROCm for stability
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 49.9 tok/s · 32K 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 49.9 tok/s · 32K 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 ~21.4 GB
397BTier 100Needs ~245.7 GB
123BTier 100Needs ~79.8 GB
1000BTier 100Needs ~615.8 GB
1000BTier 100Needs ~615.8 GB
Image & Video Generation
Diffusion Model Compatibility
24 of 52 models can generate images or video on your Radeon RX 7800M 12GB
Upgrade paths
Upgrade from Radeon RX 7800M 12GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
Radeon RX 7800M 12GBCategory AvgMacBook Pro M3 Pro 18GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~13.6s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 1s per image |
| Image Gen (SD 3.5) | Won't fit | SD 3.5 Large FP16 | ~~1m 15s per image |
| Video Short (25f) | Runs with offload | LTX Video 2B | ~~11.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~34.8s/frame |
CodeGeeX 4 9B is a specialized fit for Agentic 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 50.8 tok/s · 116K 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 49.9 tok/s · 32K ctx · llama.cppEST.
CodeGeeX 4 9B is viable for RAG, 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 50.8 tok/s · 116K ctx · llama.cppEST.
4B
6.7 GB
56 tok/s
54K ctx
Image
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 Radeon RX 7800M 12GB for local AI?
Usable for local AI with limits
Can run 10 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 1 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 Pro 18GB (18.0 GB unified memory) is the next step up.
