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URL: https://willitrunai.com/gpus/rx-6800-16gb

⇱ AI Models for RX 6800 16GB — What Runs on 16GB VRAM


AMD

RX 6800 16GB

RX 6000ConsumerRDNA 2PCIe 4ROCm
16GB
VRAM
512GB/s
Bandwidth
33TFLOPS
FP16 Compute
264TOPS
INT8 Inference
$579 MSRP
RX 6800 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.

See Full AI Tier List for RX 6800 16GB →

About this GPU for AI

The RX 6800 16GB is a high-end RDNA 2 card with 16 GB of GDDR6 VRAM, making it competitive with NVIDIA cards that cost significantly more at launch. However, RDNA 2 consumer GPUs have no official ROCm support, so AI inference relies on Vulkan backends. The 16 GB capacity enables 13B models at FP16 and 34B models at Q4, which is genuinely useful for local LLM work — if you can tolerate the software limitations.

Beyond LLMs

AI Capability Matrix

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

CapabilityStatusRepresentative ModelDetail
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~~13.7s per image
Image Gen (Flux)Won't fitFlux.1 Dev FP16~~1m 2s per image
Image Gen (SD 3.5)Runs with sequential offloadSD 3.5 Large FP16~~3m 24s per image
Video Short (25f)Runs nativelyLTX Video 2B~~11.9s/frame
Video Long (100f)Won't fitWan Video 14B~~35.1s/frame
no-rocmvulkan-onlyhigh-vramlegacy

Specifications

Compute
FP1633 TFLOPS
INT8264 TOPS
ArchitectureRDNA 2
Memory
VRAM16 GB
Bandwidth512 GB/s
General
FamilyRX 6000
SegmentConsumer
InterconnectPCIe 4
Compute PlatformROCM
MSRP$579

Key Features

RDNA 2 architecture (Navi 21 die)16 GB GDDR6 on a 256-bit bus512 GB/s memory bandwidth60 Compute UnitsAMD Infinity Cache (128 MB L3)No official ROCm — Vulkan inference only

For AI Workloads

Strengths
  • 16 GB VRAM enables 13B FP16 and 34B Q4 models without CPU offloading
  • Infinity Cache improves effective memory bandwidth for cache-resident workloads
  • Competitive used market pricing for 16 GB class hardware
  • llama.cpp Vulkan backend runs reliably on this GPU
Considerations
  • No official ROCm support — community workarounds exist but are unsupported
  • Vulkan inference is slower than ROCm or CUDA equivalents
  • PyTorch and other ML frameworks require significant setup without ROCm
  • NVIDIA RTX 3080 offers similar or better AI performance with full CUDA support

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

Buying advice

Should you buy RX 6800 16GB for local AI?

Usable for local AI with limits

Can run 11 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.

16.0 GB

VRAM

$579

MSRP

$36/GB

Cost per GB VRAM

Best models for this GPU

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.

Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.

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 55.1 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 55.1 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 55.1 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 55.1 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 62.0 tok/s · 56K ctx · llama.cppEST.
12.3 GB / 16.0 GB VRAM

Full Model Compatibility

👁 Alibaba
Qwen 3.5 9B
S95
9B10.2 GB55 tok/s58K ctx
dense
👁 Alibaba
Qwen 3 8B
S93
8B9.6 GB62 tok/s63K ctx
dense
👁 Alibaba
Qwen 3 14B
S92
14B13.5 GB36 tok/s33K ctx
dense
👁 Microsoft
Phi-4-reasoning-plus 14B
S90
14.7B14.5 GB34 tok/s24K ctx
dense
👁 Alibaba
Qwen 3.5 4B
S90
4B7.1 GB56 tok/s81K ctx
dense
👁 NVIDIA
Nemotron Nano 8B
S88
8B9.3 GB62 tok/s71K ctx
dense
👁 Mistral
Ministral 3 14B
S86
14B13.5 GB35 tok/s33K ctx
multimodal
👁 Microsoft
Phi-4 Mini Reasoning 4B
S86
3.8B6.3 GB53 tok/s122K ctx
dense
👁 OpenAI
GPT-OSS 20B
A79
21B17.8 GB33 tok/s5K ctx
moe
👁 Jina AI
Jina Embeddings v3
A78
0.57B5.6 GB8 tok/s8K ctx
dense
👁 BAAI
BGE M3
A77
0.57B4.8 GB8 tok/s8K ctx
dense
👁 Alibaba
Qwen3-Coder 30B A3B Instruct
F0
30.5B22.6 GB16 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 397B A17B
F0
397B247.5 GB2 tok/s4K ctx
moe
👁 Mistral
Devstral 2 123B Instruct
F0
123B82.9 GB2 tok/s4K ctx
dense
F0
1000B619.9 GB2 tok/s4K ctx
moe
F0
1000B619.9 GB2 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Pro
F0
1600B866.4 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 27B
F0
27B22.1 GB7 tok/s4K ctx
dense
👁 Alibaba
Qwen 3.6 27B
F0
27B19.9 GB7 tok/s4K ctx
+1dense
👁 Alibaba
Qwen 3.5 122B A10B
F0
122B79.4 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen3-VL 30B A3B Instruct
F0
30B22.3 GB17 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.6 35B A3B
F0
35B28.0 GB8 tok/s4K ctx
+1moe
👁 DeepSeek
DeepSeek V4 Flash
F0
284B161.8 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3.5 35B A3B
F0
35B25.3 GB11 tok/s4K ctx
moe
👁 Mistral
Magistral Small 2507
F0
24B19.6 GB10 tok/s4K ctx
dense
👁 Mistral
Devstral Small 2 24B Instruct
F0
24B19.6 GB10 tok/s4K ctx
dense
👁 Alibaba
Qwen 3 32B
F0
32B25.9 GB4 tok/s4K ctx
dense
👁 Alibaba
Qwen 3 30B A3B
F0
30.5B22.6 GB16 tok/s4K ctx
moe
👁 Mistral
Mistral Small 4 119B
F0
119B80.5 GB2 tok/s4K ctx
moe
👁 Cohere
Command A 111B
F0
111B74.1 GB2 tok/s4K ctx
dense
👁 Alibaba
Qwen 2.5 VL 72B
F0
72B51.3 GB2 tok/s4K ctx
dense
👁 OpenAI
GPT-OSS 120B
F0
117B78.8 GB2 tok/s4K ctx
dense
👁 NVIDIA
Nemotron 3 Nano 30B
F0
30B23.2 GB6 tok/s4K ctx
dense
👁 Alibaba
Qwen3-Coder-Next
F0
80B52.8 GB3 tok/s4K ctx
moe
👁 Mistral
Devstral Small 1.1
F0
24B19.6 GB10 tok/s4K ctx
dense
👁 Z.ai
GLM-5.1
F0
754B481.5 GB2 tok/s4K ctx
moe
👁 Mistral AI
Pixtral Large 124B
F0
124B83.5 GB2 tok/s4K ctx
dense
F0
744B475.4 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.2
F0
671B412.3 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen 3 235B A22B
F0
235B148.7 GB2 tok/s4K ctx
moe
👁 Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B298.2 GB2 tok/s4K ctx
moe
👁 NVIDIA
Nemotron Cascade 2 30B A3B
F0
30B23.7 GB14 tok/s4K ctx
moe
👁 Google
Gemma 4 31B
F0
30.7B35.9 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B146.6 GB2 tok/s4K ctx
moe
👁 Mistral
Leanstral 119B A6B
F0
119B83.9 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek Coder V2 236B
F0
236B205.1 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek R1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
👁 DeepSeek
DeepSeek V3.1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
👁 LG AI
EXAONE 4.0 32B
F0
32B25.9 GB4 tok/s4K ctx
dense
👁 Google
Gemma 4 26B A4B
F0
25.2B21.5 GB18 tok/s4K ctx
moe

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

Image & Video Generation

Diffusion Model Compatibility

31 of 52 models can generate images or video on your RX 6800 16GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~1.7sS
Stable Diffusion 1.5Image512×768~3.4sS
Realistic Vision v5.1Image512×768~3.4sS
DreamShaper 8Image512×768~3.4sS
LCM DreamShaper v7Image512×768~1sS
PixArt-SigmaImage1024×1024~13.7sS
FramePack I2VVideo256×256~25.2s/frameS
SDXL TurboImage512×512~1.7sS
SDXL LightningImage1024×1024~5.2sS
Stable Diffusion XL 1.0Image1024×1024~13.7sS
Playground v2.5Image1024×1024~20.6sS
RealVisXL v5.0Image1024×1024~15.5sS
DreamShaper XLImage1024×1024~15.5sS
Juggernaut XL v9Image1024×1024~15.5sS
Animagine XL 3.1Image1024×1024~15.5sS
Pony Diffusion V6 XLImage1024×1024~15.5sS
Animagine XL 4.0Image1024×1024~15.5sS
Illustrious XLImage1024×1024~15.5sS
Wan Video 2.1 1.3BVideo256×256~10s/frameS
Stable Diffusion 3.5 MediumImage256×256~1m 12sS
Flux.2 Klein 4BImage256×256~9.3sS
LTX Video 2BVideo256×256~11.9s/frameS
KolorsImage256×256~1m 13sA
Stable CascadeImage1024×1024~34.3sB
AuraFlow v0.3Image256×256~2m 2sB
Stable Diffusion 3.5 LargeImage256×256~3m 24sB
Stable Diffusion 3.5 Large TurboImage256×256~37.1sB
CogVideoX 2BVideo256×256~11.9s/frameD
HunyuanVideoVideo256×256~25.2s/frameD
ChromaImage256×256~13.7sD
Z-Image TurboImage256×256~28.3sD
Flux.1 DevImage256×256~1m 2sF
Flux.1 SchnellImage256×256~12sF
LTX Video 13BVideo256×256~25.2s/frameF
Flux.1 Kontext DevImage256×256~1m 9sF
AnimateDiff v1.5.3Video512×768~6.3s/frameF
Cosmos Diffusion 7BVideo256×256~19.7s/frameF
CogVideoX 5BVideo256×256~17.2s/frameF
Wan2.2 TI2V 5BVideo256×256~17.2s/frameF
Flux.2 Klein 9BImage256×256~6.9sF
Flux.1 Fill DevImage256×256~58.4sF
Mochi 1 PreviewVideo256×256~22.7s/frameF
HunyuanVideo 1.5Video256×256~21.1s/frameF
Helios 14BVideo256×256~26s/frameF
SkyReels V2 14BVideo256×256~26s/frameF
Wan Video 2.1 14BVideo256×256~26s/frameF
Wan Video 2.2 14BVideo256×256~26s/frameF
Qwen ImageImage256×256~23.1sF
Qwen Image EditImage256×256~23.1sF
Flux.2 DevImage256×256~10m 50sF
MAGI-1Video256×256~32.2s/frameF
HunyuanImage 3.0Image256×256~40.7sF

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 6800 16GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

MacBook Pro M3 24GBNext step up
24 GB Unified (+8)
C
Unlocks 2 additional models that do not fit on the current setup.Unlocks Qwen 3.6 27B, Gemma 4 26B A4B

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

~$1,099 MSRP

👁 Intel
Intel Arc Pro B60 24GBBest value
24 GB VRAM (+8)
A
Unlocks 36 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+33 more

Unlocks 36 additional models that do not fit on the current setup.

~$599 MSRP

RX 7900 XTX 24GBAMD upgrade
24 GB VRAM (+8)960 GB/s (+448)
A
Unlocks 36 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+33 more · +62% faster avg

Unlocks 36 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 62%.

~$999 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+272)8000 GB/s (+7488)
B
Unlocks 81 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+78 more · +247% faster avg

Unlocks 81 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 247%.

~$8,000 MSRP

Frequently Asked Questions

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