How fast is llama.cpp on the RX 7900 XTX?

On Linux both ROCm (HIP) and Vulkan backends work, and GGUF inference nears theoretical speed: 8B Q4 ~146 tok/s, matching the 3090's ~143. GGUF leans lightly on CUDA-specific features, so AMD bandwidth utilization holds up.

Can the 7900 XTX run LLMs on Windows?

Yes, but a tier weaker: llama.cpp's Vulkan backend works, ROCm's Windows support is limited, and frameworks like vLLM optimize for AMD well behind CUDA. Windows-first users should buy NVIDIA.

Why buy a 3090 when the 7900 XTX costs nearly half?

Ecosystem certainty: first-day CUDA framework support, dense tutorials, existing answers when things break, and a mature dual-card path to 70B (AMD multi-GPU is weak). For tinkering Linux users, the 7900 XTX is the same 24GB at half price.

GPU Comparisons / RTX 3090 vs RX 7900 XTX

RTX 3090 vs RX 7900 XTX for Local LLMs: Which Should You Pick

Verdict

Linux + willing to tinker: RX 7900 XTX — half the price, same 24GB, slightly more bandwidth. Want CUDA ecosystem certainty and a multi-GPU path: RTX 3090.

Spec Comparison

	RTX 3090	RX 7900 XTX
Nominal VRAM	24 GB	24 GB
Usable VRAM (for models)	24.0 GB	24.0 GB
Memory bandwidth	936 GB/s	960 GB/s
Type	Discrete GPU	Discrete GPU
Release year	2020	2022
MSRP	$1,499	$999
Used reference price	$1,100	$650
TDP	350 W	355 W

Model Verdict Matrix (Q4_K_M @8K)

Model	VRAM needed	RTX 3090	Theoretical speed	RX 7900 XTX	Theoretical speed
Llama 3.2 3B	4.4 GB	Comfortable	≈357	Comfortable	≈366
Llama 3.1 8B	7.5 GB	Comfortable	≈143	Comfortable	≈146
Qwen3 8B	7.7 GB	Comfortable	≈140	Comfortable	≈143
Phi-4 14B	12.2 GB	Comfortable	≈78	Comfortable	≈80
Mistral Small 3.2 24B	17.5 GB	Comfortable	≈48	Comfortable	≈49
Gemma 3 27B	22.4 GB	Tight fit	≈42	Tight fit	≈43
Qwen3.8 27B	20.7 GB	Tight fit	≈41	Tight fit	≈42
Muse Glimmer 30B	20.1 GB	Tight fit	≈39	Tight fit	≈40
Qwen3 30B-A3B	21.0 GB	Tight fit	≈347	Tight fit	≈356
Qwen3 32B	23.7 GB	Tight fit	≈35	Tight fit	≈36
gpt-oss-20b	14.7 GB	Comfortable	≈318	Comfortable	≈327
Llama 3.3 70B	47.4 GB	Not feasible	—	Not feasible	—
gpt-oss-120b	73.6 GB	Not feasible	—	Not feasible	—
DeepSeek-R1 671B	413.1 GB	Not feasible	—	Not feasible	—

Highlighted rows = watershed models where the two cards' verdicts differ. Theoretical speed = bandwidth × 0.75 ÷ per-token weight bytes (active params for MoE); real-world results vary with framework/driver/CPU, ±30%. Speed is hidden for multi-GPU/infeasible verdicts.

Value Comparison

	RTX 3090	RX 7900 XTX
Bandwidth per dollar	0.85 GB/s/$	1.48 GB/s/$
Usable VRAM per dollar	0.022 GB/$	0.037 GB/$

Pricing basis: RTX 3090 = used reference price; RX 7900 XTX = used reference price; used prices are market estimates, see analysis for volatility.

Analysis

On paper this comparison is inverted: the 7900 XTX's 960 GB/s edges the 3090's 936 by 2.6%, both have 24GB, and the 14-model verdict matrix is identical row by row (24B comfortable, 27-32B tight, 70B needs two cards) — 8B Q4 theoretically ~146 vs ~143 tok/s. Yet used prices are $600-700 vs $1,095-1,181, nearly half. Bandwidth per dollar: 1.48 vs 0.85 GB/s/$. A paper rout.

The price gap buys the CUDA ecosystem. ROCm on Linux is usable for RDNA3, llama.cpp ships HIP and Vulkan backends, and GGUF inference leans lightly on CUDA-specific features so speeds approach theoretical values. But ROCm support on Windows is limited, and AMD kernel optimizations plus multi-GPU support in frameworks like vLLM clearly lag CUDA — when things break, you read GitHub issues yourself. The 3090 brings five years of ecosystem: first-day framework support, the densest tutorials, existing answers.

Multi-GPU is another hidden watershed: dual-3090 layer-splitting for 70B is a mature recipe; AMD multi-GPU is weak and not advised. Verdict by profile: Linux-first, single-card 24B target, enjoys tinkering — the 7900 XTX is the budget shortcut to 24GB at $650; want 'every framework works out of the box' or a future multi-card path — the 3090's premium is an ecosystem tax many should pay.

FAQ

How fast is llama.cpp on the RX 7900 XTX?: On Linux both ROCm (HIP) and Vulkan backends work, and GGUF inference nears theoretical speed: 8B Q4 ~146 tok/s, matching the 3090's ~143. GGUF leans lightly on CUDA-specific features, so AMD bandwidth utilization holds up.
Can the 7900 XTX run LLMs on Windows?: Yes, but a tier weaker: llama.cpp's Vulkan backend works, ROCm's Windows support is limited, and frameworks like vLLM optimize for AMD well behind CUDA. Windows-first users should buy NVIDIA.
Why buy a 3090 when the 7900 XTX costs nearly half?: Ecosystem certainty: first-day CUDA framework support, dense tutorials, existing answers when things break, and a mature dual-card path to 70B (AMD multi-GPU is weak). For tinkering Linux users, the 7900 XTX is the same 24GB at half price.

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Data verified 2026-09-01