What AI models can AMD Radeon Pro W7900 run?

The AMD Radeon Pro W7900 can run 63 AI models. Top performers include Arcee Trinity Nano 6B, Phi-3 Mini 3.8B Instruct, Gemma 4 26B-A4B. See the full compatibility table above for speeds and quality ratings.

Is AMD Radeon Pro W7900 good for AI coding?

Yes. With 48 GB, the AMD Radeon Pro W7900 supports the Full AI Builder tier: concurrent coding + reasoning + embeddings.

How much VRAM does AMD Radeon Pro W7900 have?

The AMD Radeon Pro W7900 has 48 GB of GDDR6 VRAM with 864 GB/s bandwidth.

Can AMD Radeon Pro W7900 run 70B models?

Yes. The AMD Radeon Pro W7900 can run 70B parameter models in memory at quantized quality.

Is AMD Radeon Pro W7900 worth it for AI?

At $3,299, the AMD Radeon Pro W7900 offers 48 GB GDDR6 VRAM and runs 63 AI models. It handles local AI inference well.

Desktop GPU

AMD Radeon Pro W7900

48 GB GDDR6 · 864 GB/s

From

$3,299

Estimated street price

VRAM

48 GB

Bandwidth

864 GB/s

TDP

295W

Models

Tier

Full

The AMD Radeon Pro W7900 with 48 GB GDDR6 VRAM can handle 63 AI models across embedding, ai_building, coding. Best performance: Arcee Trinity Nano 6B at 152 tok/s (excellent). For AI coding workflows, it supports the Full AI Builder tier, supporting concurrent coding + reasoning + embeddings. Current price: approximately $3,299.

Source: OwnRig methodology

VRAM

48 GB

Bandwidth

864 GB/s

Memory Type

GDDR6

TDP

295W

Form Factor

2-slot, 267mm

Builder Capability: Full AI Builder

Supports concurrent coding + reasoning + embeddings. Can run 70B models at quantized precision.

Software

Inference Backends

The software stacks that matter most for real-world inference on this device.

ROCm

stable

Best AMD workstation backend for large-model inference and vLLM-style serving.

Vulkan

stable

Fallback llama.cpp path when ROCm-specific kernels are unavailable.

What it can run

63 models


all-MiniLM-L6-v2	FP16	–	Excellent
Arcee Trinity Mini 26B	Q8_0	36 tok/s	Good
Arcee Trinity Nano 6B	Q8_0	152 tok/s	Excellent
Code Llama 34B Instruct	Q4_K_M	24 tok/s	Good
Codestral 22B	Q5_K_M	38 tok/s	Excellent
Command R 35B	Q8_0	–	Acceptable
DeepSeek Coder V2 Lite 16B	Q5_K_M	59 tok/s	Excellent
DeepSeek R1	Q2_K	–	Not viable
DeepSeek R1 Distill Qwen 32B	Q4_K_M	18 tok/s	Good
DeepSeek R1 Distill Qwen 7B	Q8_0	41 tok/s	Good
DeepSeek V3	Q2_K	–	Not viable
FLUX.1 Dev	FP16	–	Excellent
Gemma 2 27B Instruct	Q4_K_M	24 tok/s	Good
Gemma 2 9B Instruct	Q8_0	86 tok/s	Excellent
Gemma 3 12B	Q5_K_M	81 tok/s	Excellent
Gemma 3 27B	Q5_K_M	9 tok/s	Acceptable
Gemma 3 4B	Q8_0	–	Good
Gemma 4 26B-A4B	Q8_0	134 tok/s	Excellent
Gemma 4 31B	Q8_0	19 tok/s	Acceptable
Gemma 4 E2B	Q8_0	130 tok/s	Excellent
Gemma 4 E4B	Q8_0	80 tok/s	Excellent
GigaChat Lightning 10B	Q8_0	66 tok/s	Excellent
InternLM 2.5 7B Chat	Q8_0	95 tok/s	Excellent
Llama 3.1 70B Instruct	Q4_K_M	6 tok/s	Acceptable
Llama 3.1 8B Instruct	Q8_0	35 tok/s	Good
Llama 3.2 11B Vision	Q8_0	32 tok/s	Good
Llama 3.2 1B Instruct	Q8_0	97 tok/s	Excellent
Llama 3.2 3B Instruct	Q8_0	65 tok/s	Excellent
Llama 3.3 70B Instruct	Q4_K_M	6 tok/s	Acceptable
Llama 4 Scout	Q3_K_M	2 tok/s	Marginal
LLaVA 1.6 13B	Q5_K_M	32 tok/s	Good
Mistral 7B Instruct v0.3	Q8_0	97 tok/s	Excellent
Mistral Large 2 123B	Q2_K	5 tok/s	Acceptable
Mistral Small 24B Instruct	Q5_K_M	24 tok/s	Good
Mixtral 8x7B Instruct	Q5_K_M	19 tok/s	Good
nomic-embed-text v1.5	FP16	–	Excellent
NVIDIA Nemotron-3-super-120B-A12B	Q2_K	54 tok/s	Good
Phi-3 Medium 14B Instruct	Q8_0	59 tok/s	Excellent
Phi-3 Mini 3.8B Instruct	Q8_0	140 tok/s	Excellent
Phi-4 14B	Q5_K_M	38 tok/s	Good
Phi-4 Mini	Q8_0	59 tok/s	Excellent
Qwen 2.5 14B Instruct	Q5_K_M	59 tok/s	Excellent
Qwen 2.5 72B Instruct	Q3_K_M	6 tok/s	Acceptable
Qwen 2.5 7B Instruct	Q8_0	95 tok/s	Excellent
Qwen 2.5 Coder 32B Instruct	Q4_K_M	11 tok/s	Acceptable
Qwen 2.5 Coder 7B Instruct	Q8_0	97 tok/s	Excellent
Qwen3-14B Instruct	Q8_0	27 tok/s	Good
Qwen3-30B-A3B	Q8_0	15 tok/s	Acceptable
Qwen3-32B Instruct	Q8_0	12 tok/s	Acceptable
Qwen3-8B Instruct	Q8_0	90 tok/s	Excellent
Qwen3.5-122B-A10B	Q5_K_M	39 tok/s	Good
Qwen3.5-27B	Q8_0	17 tok/s	Good
Qwen3.5-397B (MoE)	Q2_K	–	Not viable
Qwen3.6-27B	Q8_0	17 tok/s	Good
Qwen3.6-35B-A3B	Q5_K_M	15 tok/s	Acceptable
QwQ 32B Preview	Q5_K_M	18 tok/s	Good
Stable Diffusion 3 Medium	FP16	–	Excellent
Stable Diffusion 3.5 Large	FP16	–	Acceptable
Stable Diffusion XL 1.0	FP16	–	Excellent
StarCoder 2 15B	Q8_0	54 tok/s	Excellent
Whisper Large V3	FP16	–	Excellent
Whisper Large V3 Turbo	FP16	–	Excellent
Yi 1.5 34B Chat	Q8_0	–	Acceptable

Showing 63 of 63 entries

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FAQ

Frequently Asked Questions

What AI models can AMD Radeon Pro W7900 run?: The AMD Radeon Pro W7900 can run 63 AI models. Top performers include Arcee Trinity Nano 6B, Phi-3 Mini 3.8B Instruct, Gemma 4 26B-A4B. See the full compatibility table above for speeds and quality ratings.
Is AMD Radeon Pro W7900 good for AI coding?: Yes. With 48 GB, the AMD Radeon Pro W7900 supports the Full AI Builder tier: concurrent coding + reasoning + embeddings.
How much VRAM does AMD Radeon Pro W7900 have?: The AMD Radeon Pro W7900 has 48 GB of GDDR6 VRAM with 864 GB/s bandwidth.
Can AMD Radeon Pro W7900 run 70B models?: Yes. The AMD Radeon Pro W7900 can run 70B parameter models in memory at quantized quality.
Is AMD Radeon Pro W7900 worth it for AI?: At $3,299, the AMD Radeon Pro W7900 offers 48 GB GDDR6 VRAM and runs 63 AI models. It handles local AI inference well.

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