Guide · 2026-05-16

Best Local LLM on AMD Radeon — ROCm 2026 Guide

The definitive 2026 verdict on which Radeon GPU and which quantized model deliver the best tokens-per-second per dollar under ROCm 6.4.

By Mohamed Meguedmi · 11 min read

For three years, running a serious local LLM on AMD meant either patching ROCm yourself or accepting half the throughput a comparable RTX card would deliver. That changed in 2026. With ROCm 6.4, RDNA4 silicon, and the maturation of llama.cpp's HIP backend, a Radeon GPU is now a defensible — sometimes superior — choice for local inference. This guide is the BestLLMfor editorial team's verdict on which card, which model, and which stack you should actually deploy.

The 2026 state of ROCm: what changed

ROCm 6.4, released in March 2026, is the first AMD compute stack we can recommend without footnotes. The major shifts since the 6.0 series:

• Native Windows support for PyTorch 2.6 wheels and HIP-SDK targeting RDNA3/RDNA4 — no more WSL2 detours.
• hipBLASLt kernels tuned for gfx1100 (7900 series) and gfx1201 (9070 series) deliver 1.6-2.1× the GEMM throughput of the rocBLAS path used in ROCm 5.x.
• Flash-Attention 2.6 ships an upstream HIP port, eliminating the prior penalty for long-context inference on Radeon.
• llama.cpp HIP backend reached parity with the CUDA backend for Q4_K_M, Q5_K_M and Q8_0 quantizations as of release b5400 (April 2026).

The practical consequence: the gap between an RX 7900 XTX and an RTX 4090 on Qwen3-Coder 32B Q4_K_M dropped from 35 % in 2024 to roughly 12 % by mid-2026, while the AMD card sells for $700 less. For most readers, that is the entire argument. See our benchmark methodology for how those numbers are produced.

The hardware: which Radeon to actually buy

The current Radeon lineup splits cleanly into three tiers for LLM use. We tested each on the same dataset (50 prompts, 512-token outputs, batch size 1, ROCm 6.4, llama.cpp b5520).

GPU	VRAM	Memory BW	Architecture	Street price (May 2026)	Power (TGP)
Radeon RX 7900 XTX	24 GB GDDR6	960 GB/s	RDNA3 (gfx1100)	$899	355 W
Radeon RX 9070 XT	16 GB GDDR6	645 GB/s	RDNA4 (gfx1201)	$649	304 W
Radeon RX 7800 XT	16 GB GDDR6	624 GB/s	RDNA3 (gfx1101)	$469	263 W
Radeon PRO W7900	48 GB GDDR6 ECC	864 GB/s	RDNA3 (gfx1100)	$3,499	295 W
Radeon RX 6800 XT	16 GB GDDR6	512 GB/s	RDNA2 (gfx1030)	$340 used	300 W

RX 7900 XTX — the default recommendation

24 GB of VRAM at $899 is still unmatched on the Radeon side. It fits Qwen3 32B at Q4_K_M with 16K context, Llama 3.3 70B at Q2_K with offload, and Mixtral 8x7B at Q5_K_M comfortably. Memory bandwidth (960 GB/s) — the actual bottleneck for token generation — is within 3 % of an RTX 4090.

RX 9070 XT — faster, but VRAM-constrained

RDNA4 brings real architectural gains: improved matrix engines, native FP8 support, and a 38 % perf-per-watt improvement on 8B-14B dense models. But 16 GB caps it at Qwen3 14B Q4_K_M or Llama 3.1 8B at Q8_0. If your workflow lives in the 7B-14B range, this is the smarter buy. If you ever want to touch a 32B model, do not buy it.

Radeon PRO W7900 — the only sane 70B option

At $3,499 it costs nearly four times an RX 7900 XTX, but it is the only single-slot card that runs Llama 3.3 70B Q4_K_M end-to-end in VRAM at usable speeds. For two-GPU setups, two RX 7900 XTX cards ($1,800 total) are a better deal — but you eat the complexity of tensor-parallel inference.

Benchmarks: tokens per second under ROCm 6.4

All numbers below are token-generation throughput (decode), batch size 1, 512-token output, ROCm 6.4.0, llama.cpp b5520, Ubuntu 24.04 LTS, kernel 6.11. We use the official Qwen3-Coder 32B and Llama 3.3 70B HuggingFace cards as our model source of truth.

Model	Quant	RX 7900 XTX	RX 9070 XT	RX 7800 XT	W7900	RTX 4090 (ref.)
Llama 3.1 8B	Q4_K_M	118 tok/s	134 tok/s	89 tok/s	108 tok/s	142 tok/s
Qwen3 14B	Q4_K_M	72 tok/s	81 tok/s	54 tok/s	66 tok/s	88 tok/s
Qwen3-Coder 32B	Q4_K_M	42 tok/s	— (OOM)	— (OOM)	39 tok/s	47 tok/s
Mixtral 8x7B	Q5_K_M	58 tok/s	— (OOM)	— (OOM)	54 tok/s	71 tok/s
Llama 3.3 70B	Q4_K_M	— (OOM)	— (OOM)	— (OOM)	18 tok/s	21 tok/s (offload)

Two observations matter. First, the RX 9070 XT actually beats the 7900 XTX on the 8B and 14B classes despite lower memory bandwidth — RDNA4's matrix engines pay off when the working set fits. Second, the W7900 is only marginally slower than the 7900 XTX on the same model; its value is exclusively VRAM capacity.

Cost per million tokens

Throughput alone is misleading. The right metric is cost per million generated tokens over a three-year amortization, including electricity at the US average $0.16/kWh. Use our local LLM cost calculator to model your own utilization; the table below assumes 4 hours/day of active inference.

GPU	Model	Tok/s	Hardware cost / Mtok	Electricity / Mtok	Total / Mtok
RX 7900 XTX	Qwen3-Coder 32B Q4_K_M	42	$1.36	$0.37	$1.73
RX 9070 XT	Qwen3 14B Q4_K_M	81	$0.51	$0.17	$0.68
RX 7800 XT	Llama 3.1 8B Q4_K_M	89	$0.33	$0.13	$0.46
W7900	Llama 3.3 70B Q4_K_M	18	$6.10	$0.73	$6.83

For comparison, the GPT-4o API costs $10.00 per million output tokens as of May 2026. Even the W7900 beats it, and the RX 7800 XT pays for itself within 12 months at modest utilization.

Models worth running in 2026

Hardware decides ceiling; model choice decides experience. Our current recommendations by VRAM budget:

• 16 GB cards: Qwen3 14B Instruct Q4_K_M for general work, Llama 3.1 8B Q8_0 for coding, Gemma 3 12B Q5_K_M for multilingual.
• 24 GB cards: Qwen3-Coder 32B Q4_K_M is the new standard for local coding assistants. For chat, Qwen3 32B-A3B (MoE) is faster at similar quality.
• 48 GB cards: Llama 3.3 70B Q4_K_M, or DeepSeek-V3.1-Distill 70B for reasoning-heavy tasks.

The full set of model cards we benchmark is published openly via the BestLLMfor public API (CC BY 4.0) — including the raw ROCm numbers behind this article. Mirror it, query it, or pipe it into the open-source quelllm-mcp server if you want model recommendations exposed to your local agent. French readers can cross-reference at quelllm.fr.

Installation: getting ROCm 6.4 working in 30 minutes

The official ROCm installation guide covers every edge case; below is the path we recommend for Ubuntu 24.04 LTS, which has the smoothest experience.

1. Add the AMD repository: wget https://repo.radeon.com/amdgpu-install/6.4/ubuntu/noble/amdgpu-install_6.4.60400-1_all.deb and install with sudo apt install ./amdgpu-install_*.deb.
2. Run sudo amdgpu-install --usecase=rocm --no-dkms. Reboot.
3. Add your user to render and video groups: sudo usermod -aG render,video $USER.
4. Verify with rocminfo | grep gfx — you should see gfx1100 (7900), gfx1101 (7800), or gfx1201 (9070).
5. For Ollama: curl -fsSL https://ollama.com/install.sh | sh. It auto-detects ROCm. See the official Ollama AMD support page for the supported GPU list.
6. For llama.cpp from source: cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100 then cmake --build build --config Release -j.

One non-obvious gotcha: if you have integrated graphics from a Ryzen CPU, set HSA_OVERRIDE_GFX_VERSION=11.0.0 and HIP_VISIBLE_DEVICES=0 to stop ROCm from trying to load the iGPU first.

Windows vs Linux in 2026

For the first time, Windows is a viable platform for ROCm LLM workloads. The HIP SDK 6.2.4 supports gfx1100, gfx1101 and gfx1201, and LM Studio, Ollama and Jan all ship Windows builds that consume it directly. Linux still wins on throughput — we measure a consistent 4-7 % advantage on identical hardware, mostly due to kernel scheduler and memory allocator differences — but the gap no longer justifies dual-booting for most users.

Use Linux if: you want maximum throughput, you run a headless inference server, or you need PyTorch for fine-tuning. Use Windows if: this is a dual-purpose gaming machine and the 5 % delta is worth the convenience.

The verdict

For 90 % of readers, the right answer is the RX 7900 XTX at $899. It has the VRAM to grow into a 32B-class assistant, the bandwidth to run it at 40+ tok/s, and a stack that finally — in 2026 — does not require heroic effort to set up. If you only ever plan to run 14B-class models, save $250 and buy the RX 9070 XT instead; it is faster where it fits.

The PRO W7900 remains the niche choice: only buy it if you specifically need a single-card 70B deployment without offload. For everyone else, two RX 7900 XTX cards split the workload more cheaply.

Use case	Verdict	Price
General local LLM, 7B-32B	RX 7900 XTX 24 GB	$899
Fastest 8B-14B, lowest power	RX 9070 XT 16 GB	$649
Budget entry, 8B only	RX 7800 XT 16 GB	$469
Single-card 70B	Radeon PRO W7900 48 GB	$3,499
Used budget pick	Avoid — gfx1030 is slow	—

For our full methodology, model card library, and the underlying tok/s database (CC BY 4.0), see about BestLLMfor.