Radeon ProWorkstationRDNA 3PCIe 4ROCm
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 Pro W7700 16GB is a mid-range RDNA 3 workstation GPU offering 16 GB of ECC GDDR6. The workstation Pro driver stack provides more stable ROCm access than consumer RDNA 3 cards, making it a reasonable choice for enterprise AI deployments needing reliability over raw performance. The 16 GB capacity enables 13B FP16 and 34B Q4 models, suitable for most business LLM use cases.
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-supportedworkstation-gradehigh-vram
Specifications
Compute
FP1628 TFLOPS
INT856 TOPS
ArchitectureRDNA 3
Memory
VRAM16 GB
Bandwidth576 GB/s
General
FamilyRadeon Pro
SegmentWorkstation
InterconnectPCIe 4
Compute PlatformROCM
MSRP$999
Key Features
RDNA 3 architecture (Navi 32 workstation die)16 GB GDDR6 ECC on a 256-bit bus576 GB/s memory bandwidth36 Compute UnitsPCIe Gen 4 x16Full workstation ROCm support with certified drivers
For AI Workloads
Strengths
- 16 GB ECC VRAM covers 13B FP16 and 34B Q4 models in a certified workstation card
- More stable ROCm support than consumer RDNA 3 equivalents
- Suitable for production AI inference deployments requiring driver certification
- Quiet, workstation-optimized cooling for extended operation
Considerations
- Expensive ($999) for RDNA 3 — Instinct cards offer better ROCm for production at scale
- 576 GB/s bandwidth is adequate but not high for LLM decode
- RDNA 3 ROCm still trails CUDA in ecosystem completeness
- 28 TFLOPS FP16 is modest compute for a $999 card
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 52.0 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 66.5 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 Radeon PRO W7700 16GB
Upgrade paths
Upgrade from Radeon PRO W7700 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
Frequently Asked Questions
Radeon PRO W7700 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~15.1s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 8s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~3m 44s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~13.1s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~38.6s/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 66.5 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 52.0 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 74.9 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
43 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~35.4s/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 Radeon PRO W7700 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.
