AMD

RX 6950 XT 16GB

RX 6000ConsumerRDNA 2PCIe 4ROCm
16GB
VRAM
576GB/s
Bandwidth
46TFLOPS
FP16 Compute
368TOPS
INT8 Inference
$1,099 MSRP
VRAM16 GBBandwidth576 GB/sCompute46 TFInference368 TOPSValue4.19 TF/$k
RX 6950 XT 16GBCategory AvgMacBook Pro M3 24GB

Operating mode

Choose the operating mode for this hardware

Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

About this GPU for AI

The RX 6950 XT 16GB is the refreshed RDNA 2 flagship, pushing higher clocks than the 6900 XT but otherwise identical in capability. For AI use, it shares the same ROCm exclusion as the rest of the consumer RDNA 2 lineup. The clock improvements give it marginally faster token generation via Vulkan, but the fundamental software ecosystem limitations remain. At its new price, the RX 7900 XT with full ROCm support is generally a better choice.

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle — from text generation to image and video creation.

CapabilityStatusRepresentative Model
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Won’t fitQwen 3 30B Q4
LLM Large (70B)Won’t fitLlama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16
Image Gen (Flux)Won't fitFlux.1 Dev FP16
Image Gen (SD 3.5)Runs with sequential offloadSD 3.5 Large FP16
Video Short (25f)Runs nativelyLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
no-rocmvulkan-onlyhigh-vramlegacy

仕様

コンピュート
FP1646 TFLOPS
INT8368 TOPS
アーキテクチャRDNA 2
メモリ
VRAM16 GB
帯域幅576 GB/s
一般
ファミリーRX 6000
セグメントConsumer
インターコネクトPCIe 4
コンピュートプラットフォームROCM
MSRP$1,099

主な特徴

RDNA 2 architecture (Navi 21 die, maximum clock configuration)16 GB GDDR6 on a 256-bit bus576 GB/s memory bandwidth80 Compute Units at boosted clocksAMD Infinity Cache (128 MB L3)No official ROCm — same limitations as 6900 XT

AIワークロード向け

強み
  • Slightly faster than 6900 XT for Vulkan inference due to higher clocks
  • 16 GB covers most practical local LLM sizes
  • Higher memory bandwidth (576 vs 512 GB/s) improves decode throughput
  • llama.cpp Vulkan works well on this GPU
注意点
  • No official ROCm — same software limitations as all RDNA 2 consumer cards
  • Premium over 6900 XT is hard to justify for AI workloads
  • RX 7900 XT offers full ROCm support with more VRAM for comparable money
  • Lacks the AI ecosystem depth of CUDA-capable NVIDIA cards

Architecture

RDNA 2

RDNA 2 is AMD's second-generation RDNA architecture, built on TSMC 7nm. It introduced hardware ray tracing and Infinity Cache for improved bandwidth efficiency. Powers the RX 6000 series and is also used in gaming consoles.

AI Relevance

Limited official ROCm support for consumer RDNA 2 cards — most AI runtimes require workarounds. Can run smaller models via llama.cpp with Vulkan or HIP backends, but performance is well behind NVIDIA equivalents.

Process: TSMC 7nmPlatform: ROCMPrecisions: FP32, FP16, INT8

購入アドバイス

ローカルAIにRX 6950 XT 16GBを買うべき?

制限付きでローカルAIに使用可能

上位50モデル中11モデルを実行可能(主に小規模)。大規模モデルには強い量子化が必要か、適合しません。

16.0 GB

VRAM

$1,099

希望小売価格

$69/GB

GBあたりのコスト

このGPUに最適なモデル

What will limit you first

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best upgrade itinerary

Unlocks 2 additional models that do not fit on the current setup.

もっと余裕が欲しいですか? MacBook Pro M3 24GB (24.0 GB unified memory) が次のステップアップです。

Recommendations by Workload

Chat

S

Qwen 3.5 9B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 65.4 tok/s · 58K ctx · llama.cppEST.
9.1 GB / 16.0 GB VRAM

Coding

S

Qwen 3.5 9B

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 65.4 tok/s · 58K ctx · llama.cppEST.
10.2 GB / 16.0 GB VRAM

Agentic Coding

S

Qwen 3.5 9B

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 65.4 tok/s · 58K ctx · llama.cppEST.
12.4 GB / 16.0 GB VRAM

Reasoning

S

Qwen 3.5 9B

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 65.4 tok/s · 58K ctx · llama.cppEST.
10.2 GB / 16.0 GB VRAM

RAG

A

Granite 4.1 8B

This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.

Decode 73.6 tok/s · 56K ctx · llama.cppEST.
12.3 GB / 16.0 GB VRAM

Full Model Compatibility

AlibabaQwen 3.5 9B
S96
9B10.2 GB65 tok/s58K ctx
dense
AlibabaQwen 3 8B
S94
8B9.6 GB74 tok/s63K ctx
dense
AlibabaQwen 3 14B
S92
14B13.5 GB42 tok/s33K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
S91
14.7B14.5 GB40 tok/s24K ctx
dense
AlibabaQwen 3.5 4B
S90
4B7.1 GB56 tok/s81K ctx
dense
NVIDIANemotron Nano 8B
S89
8B9.3 GB74 tok/s71K ctx
dense
MistralMinistral 3 14B
S86
14B13.5 GB42 tok/s33K ctx
multimodal
MicrosoftPhi-4 Mini Reasoning 4B
S86
3.8B6.3 GB53 tok/s122K ctx
dense
OpenAIGPT-OSS 20B
A80
21B17.8 GB39 tok/s5K ctx
moe
Jina AIJina Embeddings v3
A78
0.57B5.6 GB8 tok/s8K ctx
dense
BAAIBGE M3
A77
0.57B4.8 GB8 tok/s8K ctx
dense
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B22.6 GB18 tok/s4K ctx
moe
AlibabaQwen 3.5 397B A17B
F0
397B247.5 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B82.9 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B619.9 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B619.9 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B866.4 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 27B
F0
27B22.1 GB8 tok/s4K ctx
dense
AlibabaQwen 3.6 27B
F0
27B19.9 GB8 tok/s4K ctx
+1dense
AlibabaQwen 3.5 122B A10B
F0
122B79.4 GB2 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B22.3 GB20 tok/s4K ctx
moe
AlibabaQwen 3.6 35B A3B
F0
35B28.0 GB10 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Flash
F0
284B161.8 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 35B A3B
F0
35B25.3 GB13 tok/s4K ctx
moe
MistralMagistral Small 2507
F0
24B19.6 GB12 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B19.6 GB12 tok/s4K ctx
dense
AlibabaQwen 3 32B
F0
32B25.9 GB5 tok/s4K ctx
dense
AlibabaQwen 3 30B A3B
F0
30.5B22.6 GB18 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B80.5 GB2 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B74.1 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B51.3 GB2 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B78.8 GB2 tok/s4K ctx
dense
NVIDIANemotron 3 Nano 30B
F0
30B23.2 GB7 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B52.8 GB3 tok/s4K ctx
moe
MistralDevstral Small 1.1
F0
24B19.6 GB12 tok/s4K ctx
dense
Z.aiGLM-5.1
F0
754B481.5 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B83.5 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B475.4 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B412.3 GB2 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B148.7 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B298.2 GB2 tok/s4K ctx
moe
NVIDIANemotron Cascade 2 30B A3B
F0
30B23.7 GB17 tok/s4K ctx
moe
GoogleGemma 4 31B
F0
30.7B35.9 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B146.6 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B83.9 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B205.1 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
LG AIEXAONE 4.0 32B
F0
32B25.9 GB5 tok/s4K ctx
dense
GoogleGemma 4 26B A4B
F0
25.2B21.5 GB22 tok/s4K ctx
moe

もう少しで届く

アップグレードで動くモデル

もう少しメモリがあれば動く高品質モデル

Image & Video Generation

Diffusion Model Compatibility

31 of 52 models can generate images or video on your RX 6950 XT 16GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~1.2sS
Stable Diffusion 1.5Image512×768~2.3sS
Realistic Vision v5.1Image512×768~2.3sS
DreamShaper 8Image512×768~2.3sS
LCM DreamShaper v7Image512×768700msS
PixArt-SigmaImage1024×1024~9.3sS
FramePack I2VVideo256×256~17.2s/frameS
SDXL TurboImage512×512~1.2sS
SDXL LightningImage1024×1024~3.5sS
Stable Diffusion XL 1.0Image1024×1024~9.3sS
Playground v2.5Image1024×1024~14sS
RealVisXL v5.0Image1024×1024~10.5sS
DreamShaper XLImage1024×1024~10.5sS
Juggernaut XL v9Image1024×1024~10.5sS
Animagine XL 3.1Image1024×1024~10.5sS
Pony Diffusion V6 XLImage1024×1024~10.5sS
Animagine XL 4.0Image1024×1024~10.5sS
Illustrious XLImage1024×1024~10.5sS
Wan Video 2.1 1.3BVideo256×256~6.8s/frameS
Stable Diffusion 3.5 MediumImage256×256~49.1sS
Flux.2 Klein 4BImage256×256~6.3sS
LTX Video 2BVideo256×256~8.1s/frameS
KolorsImage256×256~49.6sA
Stable CascadeImage1024×1024~23.4sB
AuraFlow v0.3Image256×256~1m 23sB
Stable Diffusion 3.5 LargeImage256×256~2m 19sB
Stable Diffusion 3.5 Large TurboImage256×256~25.2sB
CogVideoX 2BVideo256×256~8.1s/frameD
HunyuanVideoVideo256×256~17.2s/frameD
ChromaImage256×256~9.3sD
Z-Image TurboImage256×256~19.3sD
Flux.1 DevImage256×256~42sF
Flux.1 SchnellImage256×256~8.2sF
LTX Video 13BVideo256×256~17.2s/frameF
Flux.1 Kontext DevImage256×256~46.7sF
AnimateDiff v1.5.3Video512×768~4.3s/frameF
Cosmos Diffusion 7BVideo256×256~13.4s/frameF
CogVideoX 5BVideo256×256~11.7s/frameF
Wan2.2 TI2V 5BVideo256×256~11.7s/frameF
Flux.2 Klein 9BImage256×256~4.7sF
Flux.1 Fill DevImage256×256~39.7sF
Mochi 1 PreviewVideo256×256~15.4s/frameF
HunyuanVideo 1.5Video256×256~14.3s/frameF
Helios 14BVideo256×256~17.7s/frameF
SkyReels V2 14BVideo256×256~17.7s/frameF
Wan Video 2.1 14BVideo256×256~17.7s/frameF
Wan Video 2.2 14BVideo256×256~17.7s/frameF
Qwen ImageImage256×256~15.7sF
Qwen Image EditImage256×256~15.7sF
Flux.2 DevImage256×256~7m 22sF
MAGI-1Video256×256~21.9s/frameF
HunyuanImage 3.0Image256×256~27.7sF

Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.

Upgrade paths

Upgrade from RX 6950 XT 16GB

See what you unlock with more powerful hardware

アップグレードオプション

アップグレードオプション

Frequently Asked Questions

What AI models can I run on RX 6950 XT 16GB?

RX 6950 XT 16GB (16 GB VRAM) can run these top models: Qwen 3.5 9B (score: 96/100), Qwen 3 8B (score: 94/100), Qwen 3 14B (score: 92/100). See the full compatibility list above.

How much VRAM does RX 6950 XT 16GB have for AI?

RX 6950 XT 16GB has 16 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is RX 6950 XT 16GB good for running LLMs locally?

Yes, RX 6950 XT 16GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RX 6950 XT 16GB for coding?

For coding on RX 6950 XT 16GB, we recommend Qwen 3.5 9B. It achieves 65.4 tokens per second with 58K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Should I upgrade from RX 6950 XT 16GB?

There are 4 upgrade path(s) from RX 6950 XT 16GB: MacBook Pro M3 24GB, Intel Arc Pro B60 24GB. Upgrading would unlock larger models and faster inference speeds.

Can RX 6950 XT 16GB run Flux for image generation?

RX 6950 XT 16GB can run Flux.1 Dev with sequential offloading or at a lower precision (FP8/NF4). The Schnell variant is faster and fits more easily. For best results, use ComfyUI with model offloading enabled.

What image and video AI models can I run on RX 6950 XT 16GB?

RX 6950 XT 16GB (16 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.

Is RX 6950 XT 16GB good for AI image generation?

RX 6950 XT 16GB is good for AI image generation. It handles SDXL and SD 3.5 well, and can run Flux with some optimization. 16 GB of usable memory is sufficient for most image generation workflows at standard resolutions.

Can RX 6950 XT 16GB run Qwen 3.5 27B?

Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, which is tight for RX 6950 XT 16GB with 16 GB. You can run the 9B variant at Q8 (9.6 GB) for excellent quality, or try the 35B-A3B MoE variant at Q4 if it fits your context needs.

What is the best quantization for AI models on RX 6950 XT 16GB?

With 16 GB on RX 6950 XT 16GB, use Q8_0 for 8B models (best quality), Q4_K_M for 14B models (good balance), and Q4_K_M with limited context for larger models. Avoid going below Q4 — quality drops sharply at Q2-Q3.

For local LLMs on RX 6950 XT 16GB, does VRAM matter more than bandwidth?

RX 6950 XT 16GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.

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