Best Local AI Models for AMD RX 7900 XTX (24GB)

The RX 7900 XTX is the AMD counterpart to the RTX 3090: 24GB of VRAM at 960 GB/s for roughly $600-800 on the used market. That capacity runs 32B parameter models entirely in VRAM, and since token generation is bandwidth-bound, its 960 GB/s actually edges out the 3090's 936 GB/s. Ollama and llama.cpp both support it via ROCm, with Vulkan as a fallback, so the CUDA-free stack is no longer the obstacle it once was.

24GB VRAM

Quick answer

The best local LLM for the RX 7900 XTX is Gemma 4 26B-A4B at ~72 tok/s on its 24GB VRAM. It uses ~16GB of VRAM; the RX 7900 XTX handles up to 32b parameter models at Q4. A 32B model runs at ~27 tok/s.

$ollama run gemma4:26b

TOP PICK

Gemma 4 26B-A4B

EST. SPEED

~72 tok/s

VRAM NEEDED

~16 GB

Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.

VRAM24 GB GDDR6

Speed (8B Q4)89 tok/s

Bandwidth960 GB/s

ArchitectureRDNA 3

Price$700*

Max model sizeUp to 32B parameter models

Compatibility10 excellent, 0 workable

*Used market price

RX 7900 XTX Estimated Tokens/sec by Model Size

Q4_K_M · ModelFit estimate

Model Size	Est. Speed	Fit on 24GB
7B	~100 tok/s	Fits in VRAM
14B	~55 tok/s	Fits in VRAM
20B MoE (3.6B active)	~85 tok/s	Fits in VRAM
32B	~27 tok/s	Fits in VRAM
35B MoE (3B active)	~72 tok/s	Fits in VRAM
70B	~1 tok/s	CPU offload (slow)
120B MoE (5.1B active)	~12 tok/s	CPU offload (slow)

ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RX 7900 XTX's 960 GB/s bandwidth, then scaled by model size. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 24GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.

Context costs VRAM too. Gemma 4 26B-A4B loads ~16 GB of weights; at 16k context the KV cache adds ~4.0 GB (still fits the ~22 GB usable VRAM), and at 64k it adds ~16.0 GB (exceeds the budget, use a smaller quant or a q8_0 KV cache).

KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.

Where to Buy the RX 7900 XTX

≈ $700 street · Used market price

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Storage & accessories for your model library

Internal NVMe SSD · 2TB~$170

A Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.

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USB4 NVMe Enclosure~$80

40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.

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ModelFit may earn a commission on purchases through these links, at no extra cost to you. Prices shown are approximate street references.

RX 7900 XTX VRAM for AI: What Actually Fits?

24GB GDDR6 at 960 GB/s puts the RX 7900 XTX in the same capacity class as the RTX 3090 and RTX 4090: 32B models at Q4 (~20GB) load fully in VRAM with room for context. RDNA 3 (gfx1100) is officially supported by ROCm, which Ollama uses on Linux and Windows; llama.cpp also runs it via ROCm/HIP or Vulkan. Where the card really stands out is dual-card rigs: two 7900 XTX give 48GB of pooled VRAM for around $1,400 used, enough to hold a 35B-class MoE at Q8 with its full context, a budget no NVIDIA pair matches. The trade-off versus NVIDIA is software maturity outside llama.cpp: some backends (vLLM, ExLlama) still favor CUDA.

RX 7900 XTX vs Top GPUs

Hardware	Memory	Speed	Bandwidth	Price
RX 7900 XT	20 GB	74 tok/s	800 GB/s	$550
RX 7900 XTX	24 GB	89 tok/s	960 GB/s	$700
RTX 3090	24 GB	87 tok/s	936 GB/s	$900
RTX 4090	24 GB	104 tok/s	1008 GB/s	$2,574

Fits in 24 GB VRAM with room to spare. Best for quality, coding, reasoning on RX 7900 XTX.

ollamaregistry-verified

Models Too Big for 24GB? Rent a Cloud GPU

by the hour

The RX 7900 XTX tops out around up to 32b parameter models. For anything bigger, an hourly rented GPU runs the same open weights with the same Ollama workflow, billed by the hour, no hardware purchase needed.

RunPodHourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.Rent

Vast.aiMarketplace of rented GPUs, usually the cheapest per-hour prices.Rent

ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.

Compare With Top GPUs

RTX 3090 (24GB · 87 tok/s)RTX 4090 (24GB · 104 tok/s)RX 7900 XT (20GB · 74 tok/s)

Compatible Model Families

Qwen

Alibaba Cloud: Widest size range (0.5B to 235B)

Llama

Meta: Most popular open-weight model family

DeepSeek

DeepSeek AI: Best-in-class reasoning with R1 models

Mistral

Mistral AI: Excellent performance-per-parameter ratio

Gemma

Google DeepMind: Excellent quality at small sizes (1B-9B)

Phi

Microsoft: Best quality-per-gigabyte at small sizes

RX 7900 XTX FAQ: Common Questions

What size LLM can I run on an RX 7900 XTX?

Up to 32B parameter models at Q4 quantization. Its 24GB VRAM gives about 21.6GB usable for weights, the same capacity class as the RTX 3090 and RTX 4090. Good picks are Qwen3.6 27B and DeepSeek-R1 32B. 35B-class MoE models at Q4 (~20-22GB) sit right at the limit: Qwen3.5 35B-A3B fits, Qwen3.6 35B-A3B may spill slightly to system RAM.

Does Ollama work on the RX 7900 XTX?

Yes. Ollama supports the RX 7900 XTX through ROCm on Linux and Windows, since RDNA 3 (gfx1100) is on the official ROCm support list. llama.cpp and LM Studio also run it via ROCm/HIP or the Vulkan backend. No CUDA is needed.

Can two RX 7900 XTX run a 35B model at Q8 with full context?

Yes. Two cards pool 48GB of VRAM, and a 35B-class MoE at Q8 weighs about 38.7GB. Hybrid linear-attention models like Qwen3.6 35B-A3B cache KV only on their sparse full-attention layers, roughly 20 KB per token, so even the full 262k context adds only ~5GB. Weights plus full context fit inside the two cards with no offloading.

RX 7900 XTX vs RTX 3090 for local AI?

They are close. Both have 24GB and run the same 32B-class models; the 7900 XTX has slightly higher bandwidth (960 vs 936 GB/s) and tends to cost less used. The 3090 keeps the edge in software ecosystem: CUDA-only backends and broader community tooling. For an Ollama/llama.cpp workflow, either works well.

Is ROCm still a problem for local LLMs in 2026?

Not for the main runtimes. Ollama, llama.cpp, and LM Studio all support RDNA 3 through ROCm or Vulkan, and the 7900 XTX is on the official ROCm support list. Friction remains in CUDA-first projects like some vLLM and training workflows, so check your specific stack before buying.

How fast is a 27B-class model like Qwen3.5 27B Instruct on the RX 7900 XTX?

ModelFit estimates a 32B model on the RX 7900 XTX runs at roughly 27 tok/s at Q4_K_M. The current 27B-class pick in the catalog is Qwen3.5 27B Instruct (ollama run qwen3.5:27b).