What Models?

Pick the right model for your GPU — in seconds.

What AI models can a Radeon RX 9060 XT 16GB (16 GB) run?

The Radeon RX 9060 XT 16GB has 16 GB of VRAM and 320 GB/s memory bandwidth. 41 models run well on it and 8 more are a tight fit.

Fine-tune for your setup (context, speed, system RAM, multiple GPUs) → Check price on Amazon

As an Amazon Associate, I earn from qualifying purchases.

Best for general use

Highest MMLU score among models that run well

  1. Phi-4 14B Q4_K_M 16K context · ~32 tok/s · MMLU 84.8
  2. Qwen3.5 9B Q8_0 230K context · ~32 tok/s · MMLU 82.5
  3. Qwen 2.5 14B Q4_K_M 38K context · ~32 tok/s · MMLU 79.9

Best for coding

Highest SWE-bench Verified score among models that run well

  1. Muse Glimmer 30B Q4_K_M 32K context · ~19 tok/s · SWE-bench 76
  2. K2-Horizon 7B Q8_0 51K context · ~32 tok/s · SWE-bench 70.6
  3. Ornith 1.5 9B Q8_0 222K context · ~32 tok/s · SWE-bench 70.6

Upgrade options

The cheapest GPUs with more VRAM than the Radeon RX 9060 XT 16GB, by launch MSRP.

As an Amazon Associate, I earn from qualifying purchases.

Every model on the Radeon RX 9060 XT 16GB

89 of 200 model configurations can run on 16 GB — sorted by quality

100 models, each counted once by the best way it runs on your hardware.

Largest that runs well Muse Glimmer 30B 29.78B params · Q4_K_M · 32K context
Fastest good-quality model Phi-3 Mini 3.8B ~94 tok/s · Q4_K_M · 37K context
? MMLU (Massive Multitask Language Understanding) measures general knowledge across 57 subjects. Higher = more capable. Sorted best-first.

Runs well 41

  • Phi-4 14B 14.7B Excellent
    MMLU 84.8
    Q4_K_M
    Context 16K Speed ~32 tok/s
    Q8_0 Tight fit · 2K context
  • Qwen3.5 9B 9B Great VisionTool useReasoning
    MMLU 82.5
    Q4_K_M
    Context 256K Speed ~51 tok/s
    Q8_0
    Context 230K Speed ~32 tok/s
  • Qwen 2.5 14B 14.7B Great Tool use
    MMLU 79.9
    Q4_K_M
    Context 38K Speed ~32 tok/s
    Q8_0 Tight fit · 1K context

Runs well 41

  • Phi-4 14B 14.7B Excellent
    MMLU 84.8
    Q4_K_M
    Context 16K Speed ~32 tok/s
    Q8_0 Tight fit · 2K context
  • Qwen3.5 9B 9B Great VisionTool useReasoning
    MMLU 82.5
    Q4_K_M
    Context 256K Speed ~51 tok/s
    Q8_0
    Context 230K Speed ~32 tok/s
  • Qwen 2.5 14B 14.7B Great Tool use
    MMLU 79.9
    Q4_K_M
    Context 38K Speed ~32 tok/s
    Q8_0 Tight fit · 1K context
  • Qwen3.5 4B 4B Great VisionTool useReasoning
    MMLU 79.1
    Q4_K_M
    Context 256K Speed ~90 tok/s
    Q8_0
    Context 256K Speed ~62 tok/s
  • Qwen3 8B 8.2B Great ReasoningTool use
    MMLU 79
    Q4_K_M
    Context 80K Speed ~53 tok/s
    Q8_0
    Context 53K Speed ~33 tok/s
  • Gemma 4 12B 11.96B Great VisionTool useReasoning
    MMLU 77.2
    Q4_K_M
    Context 256K Speed ~42 tok/s
    Q8_0
    Context 256K Speed ~25 tok/s
  • Nemotron Nano 12B 12.6B Great ReasoningTool use
    MMLU 76
    Q4_K_M
    Context 128K Speed ~38 tok/s
    Q8_0
    Context 126K Speed ~23 tok/s
  • GPT-OSS 20B 21B Great ReasoningTool use
    MMLU 75
    Q4_K_M
    Context 128K Speed ~25 tok/s
    Q8_0
    Context 128K Speed ~24 tok/s
  • Qwen 2.5 7B 7.62B Good Tool use
    MMLU 74.2
    Q4_K_M
    Context 128K Speed ~56 tok/s
    Q8_0
    Context 128K Speed ~35 tok/s
  • Qwen 2.5 VL 7B 7.62B Good VisionTool use
    MMLU 74.2
    Q4_K_M
    Context 128K Speed ~56 tok/s
    Q8_0
    Context 128K Speed ~35 tok/s
  • Gemma 3 12B 11.8B Good Vision
    MMLU 74
    Q4_K_M
    Context 128K Speed ~39 tok/s
    Q8_0
    Context 57K Speed ~24 tok/s
  • Qwen 2.5 Coder 14B 14.7B Good
    MMLU 74
    Q4_K_M
    Context 38K Speed ~32 tok/s
    Q8_0 Tight fit · 1K context
  • Llama 3.2 Vision 11B 10.6B Good VisionTool use
    MMLU 73
    Q4_K_M
    Context 82K Speed ~46 tok/s
    Q8_0
    Context 45K Speed ~28 tok/s
  • DeepSeek R1 Distill Qwen 14B 14.7B Good Reasoning
    MMLU 72
    Q4_K_M
    Context 38K Speed ~32 tok/s
    Q8_0 Tight fit · 1K context
  • Phi-3 Mini 3.8B 3.82B Good
    MMLU 70.9
    Q4_K_M
    Context 37K Speed ~94 tok/s
    Q8_0
    Context 32K Speed ~63 tok/s
  • Phi-4 Mini 3.8B 3.84B Good
    MMLU 70
    Q4_K_M
    Context 110K Speed ~92 tok/s
    Q8_0
    Context 97K Speed ~63 tok/s
  • Gemma 4 E4B 4.5B Good VisionTool useReasoning
    MMLU 69.4
    Q4_K_M
    Context 128K Speed ~53 tok/s
    Q8_0
    Context 128K Speed ~38 tok/s
  • Llama 3.1 8B 8.03B Good Tool use
    MMLU 69.4
    Q4_K_M
    Context 90K Speed ~54 tok/s
    Q8_0
    Context 61K Speed ~34 tok/s
    fp16 Doesn't fit · needs 16.2 GB
  • Qwen 2.5 Coder 7B 7.62B Good
    MMLU 68
    Q4_K_M
    Context 128K Speed ~56 tok/s
    Q8_0
    Context 128K Speed ~35 tok/s
  • Mistral Nemo 12B 12.2B Good Tool use
    MMLU 68
    Q4_K_M
    Context 43K Speed ~37 tok/s
    Q8_0 Tight fit · 15K context
  • Qwen 2.5 VL 3B 3.09B Fair Vision
    MMLU 65.6
    Q4_K_M
    Context 128K Speed ~109 tok/s
    Q8_0
    Context 128K Speed ~75 tok/s
  • Qwen 2.5 3B 3.09B Fair Tool use
    MMLU 65.6
    Q4_K_M
    Context 32K Speed ~109 tok/s
    Q8_0
    Context 32K Speed ~75 tok/s
  • Ministral 8B 8.02B Fair Tool use
    MMLU 65
    Q4_K_M
    Context 79K Speed ~53 tok/s
    Q8_0
    Context 54K Speed ~34 tok/s
  • SmallThinker 3B 3.09B Fair Reasoning
    MMLU 65
    Q4_K_M
    Context 32K Speed ~109 tok/s
    Q8_0
    Context 32K Speed ~75 tok/s
  • Llama 3.2 3B 3.21B Fair Tool use
    MMLU 63.4
    Q4_K_M
    Context 128K Speed ~106 tok/s
    Q8_0
    Context 117K Speed ~72 tok/s
    fp16
    Context 89K Speed ~43 tok/s
  • Gemma 3 4B 3.88B Fair Vision
    MMLU 63
    Q4_K_M
    Context 128K Speed ~92 tok/s
    Q8_0
    Context 128K Speed ~62 tok/s
  • Gemma 4 E2B 2.3B Fair VisionTool useReasoning
    MMLU 60
    Q4_K_M
    Context 128K Speed ~76 tok/s
    Q8_0
    Context 128K Speed ~57 tok/s
  • DeepSeek R1 Distill Qwen 7B 7.62B Fair Reasoning
    MMLU 58
    Q4_K_M
    Context 128K Speed ~56 tok/s
    Q8_0
    Context 128K Speed ~35 tok/s
  • Qwen3.5 2B 2B Fair VisionTool useReasoning
    MMLU 55.3
    Q4_K_M
    Context 256K Speed ~146 tok/s
    Q8_0
    Context 256K Speed ~111 tok/s
  • StarCoder2 15B 15.6B Basic
    MMLU 51.4
    Q4_K_M
    Context 16K Speed ~29 tok/s
    Q8_0 Doesn't fit · needs 17.1 GB
  • DeepSeek Coder 6.7B 6.74B Basic
    MMLU 50
    Q4_K_M
    Context 16K Speed ~63 tok/s
    Q8_0
    Context 16K Speed ~39 tok/s
  • Llama 3.2 1B 1.24B Basic Tool use
    MMLU 49.3
    Q4_K_M
    Context 128K Speed ~177 tok/s
    Q8_0
    Context 128K Speed ~138 tok/s
  • Qwen3.5 0.8B 0.8B Basic VisionTool useReasoning
    MMLU 29.7
    Q4_K_M
    Context 256K Speed ~213 tok/s
    Q8_0
    Context 256K Speed ~182 tok/s
  • Nemotron 3 Nano 4B 3.97B Basic ReasoningTool use
    MMLU
    Q4_K_M
    Context 256K Speed ~85 tok/s
    Q8_0
    Context 256K Speed ~65 tok/s
  • Ornith 1.5 9B 9.65B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 256K Speed ~50 tok/s
    Q8_0
    Context 222K Speed ~32 tok/s
  • MiMo V2.6 Distill Qwen 9B 9.41B Basic VisionTool useReasoning
    MMLU
    Q4_K_M
    Context 256K Speed ~50 tok/s
    Q8_0
    Context 229K Speed ~32 tok/s
  • LFM2.5 2.6B 2.7B Basic Tool use
    MMLU
    Q4_K_M
    Context 128K Speed ~125 tok/s
    Q8_0
    Context 128K Speed ~87 tok/s
  • LFM2.5 24B A2B 23.97B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 128K Speed ~134 tok/s

    MoE — speed based on ~1.38 GB active experts (full 13.53 GB loaded in VRAM)

    Q8_0 Doesn't fit · needs 23.8 GB
  • K2-Horizon 7B 9B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 78K Speed ~52 tok/s
    Q8_0
    Context 51K Speed ~32 tok/s
  • Nemotron 3.5 Bolt E4B 8B Basic Tool useReasoning
    MMLU
    Q4_K_M
    Context 64K Speed ~47 tok/s
    Q8_0
    Context 64K Speed ~38 tok/s
  • Muse Glimmer 30B 29.78B Basic VisionTool useReasoning
    MMLU
    Q4_K_M
    Context 32K Speed ~19 tok/s
    Q8_0 Doesn't fit · needs 27.6 GB

Tight fit 8

Tight fit 8

Notify me when new models fit my GPU

An email when a new model fits your Radeon RX 9060 XT 16GB — no spam, unsubscribe anytime.

Doesn't fit 51

  • DeepSeek R1 0528 671B Excellent Reasoning
    MMLU 90.8
    Q4_K_M Needs 409.3 GB — 393.3 GB over
    Q8_0 Needs 713.4 GB — 697.4 GB over
  • Qwen3 Next 80B-A3B 80B Excellent Tool use
    MMLU 90
    Q4_K_M Needs 48.4 GB — 32.4 GB over
    Q8_0 Needs 84.8 GB — 68.8 GB over
    GPUs that can run this

    As an Amazon Associate, I earn from qualifying purchases.

  • MiMo V2.5 Pro 1020B Excellent Reasoning
    MMLU 89.4
    Q4_K_M Needs 630.1 GB — 614.1 GB over
    Q8_0 Needs 1090.1 GB — 1074.1 GB over

Doesn't fit 51