Best Local AI Models for AMD RX 7900 XT (20GB)

The RX 7900 XT sits in a capacity gap NVIDIA does not serve: 20GB of VRAM, more than any 16GB card and cheaper than any 24GB one. That extra 4GB over a 5070 Ti lets 27B-class models load fully in VRAM at Q4, and 800 GB/s of bandwidth keeps generation quick. Like the XTX, it runs Ollama and llama.cpp through ROCm with a Vulkan fallback.

20GB VRAM

Quick answer

The best local LLM for the RX 7900 XT is GPT-OSS 20B at ~62 tok/s on its 20GB VRAM. It uses ~13.8GB of VRAM; the RX 7900 XT handles up to 27b parameter models at Q4. A 14B model runs at ~46 tok/s.

$ollama run gpt-oss:20b

TOP PICK

GPT-OSS 20B

EST. SPEED

~62 tok/s

VRAM NEEDED

~13.8 GB

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

VRAM20 GB GDDR6

Speed (8B Q4)74 tok/s

Bandwidth800 GB/s

ArchitectureRDNA 3

Price$550*

Max model sizeUp to 27B parameter models

Compatibility10 excellent, 0 workable

*Used market price

RX 7900 XT Estimated Tokens/sec by Model Size

Q4_K_M · ModelFit estimate

Model Size	Est. Speed	Fit on 20GB
7B	~83 tok/s	Fits in VRAM
14B	~46 tok/s	Fits in VRAM
20B MoE (3.6B active)	~70 tok/s	Fits in VRAM
32B	~5 tok/s	CPU offload (slow)
35B MoE (3B active)	~45 tok/s	CPU offload (slow)
70B	~1 tok/s	CPU offload (slow)
120B MoE (5.1B active)	~10 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 XT's 800 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 20GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.

Context costs VRAM too. GPT-OSS 20B loads ~13.8 GB of weights; at 16k context the KV cache adds ~4.0 GB (still fits the ~18 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 XT

≈ $550 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 XT VRAM for AI: What Actually Fits?

20GB GDDR6 at 800 GB/s is a unique spot in the lineup: 16GB cards top out at 14B-class dense models, while the 7900 XT fits a 27B at Q4 (~18GB) right at its usable budget, with the KV cache limiting context length. It cannot hold 32B Q4 (~20GB), so treat 27B as the practical ceiling. RDNA 3 is on the official ROCm support list, so Ollama, llama.cpp, and LM Studio all run it without CUDA. On the used market it usually undercuts every 24GB card by $150-300, making it a strong value pick for the 14B-27B class.

RX 7900 XT 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 5070 Ti	16 GB	87 tok/s	896 GB/s	$749
RTX 3090	24 GB	87 tok/s	936 GB/s	$900

Fits in 20 GB VRAM with room to spare. Best for coding, quality, long context on RX 7900 XT.

ollamaregistry-verified

Models Too Big for 20GB? Rent a Cloud GPU

by the hour

The RX 7900 XT tops out around up to 27b 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

RX 7900 XTX (24GB · 89 tok/s)RTX 5070 Ti (16GB · 87 tok/s)RTX 3090 (24GB · 87 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 XT FAQ: Common Questions

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

Up to 27B parameter models at Q4 quantization. Its 20GB VRAM gives about 18GB usable, which holds a 27B Q4 (~18GB) fully in VRAM with context kept modest. 32B models need CPU offloading, which cuts speed sharply for dense models.

Does Ollama support the RX 7900 XT?

Yes. RDNA 3 (gfx1100) is officially supported by ROCm, which Ollama uses on Linux and Windows. llama.cpp and LM Studio also work via ROCm/HIP or Vulkan.

RX 7900 XT vs RTX 5070 Ti for local AI?

The 7900 XT has 4GB more VRAM (20 vs 16GB), so it runs 27B-class models the 5070 Ti cannot fit. The 5070 Ti has higher bandwidth (896 vs 800 GB/s) and the CUDA ecosystem, so it is faster on the 14B class both can hold. Pick by target model size.

RX 7900 XT or RX 7900 XTX for local LLMs?

The XTX if the budget allows: 24GB vs 20GB moves the ceiling from 27B to 32B-class models, and its 960 GB/s is 20% faster. The XT wins on value when the models you want stay at or under 27B.

How fast is a 27B-class model on the RX 7900 XT?

The RX 7900 XT's 20GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 20B MoE (3.6B active), at an estimated 70 tok/s.