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URL: https://willitrunai.com/gpus/arc-a380-6gb

⇱ AI Models for Intel Arc A380 6GB β€” What Runs on 6GB VRAM


Intel

Intel Arc A380 6GB

Arc AConsumerAlchemistPCIe 4oneAPI

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 Intel Arc A380 6GB β†’

About this GPU for AI

The Arc A380 6GB is Intel's entry-level Alchemist discrete GPU, positioned as an affordable upgrade from integrated graphics for mainstream desktops. At $139 it was one of Intel's first discrete Arc GPUs to reach the consumer market. For AI inference, the 6 GB GDDR6 limits practical use to 3B models at FP16 or 7B at Q4 with CPU offloading. It serves as an entry point to the Intel oneAPI SYCL ecosystem for users curious about Intel's AI acceleration path.

Beyond LLMs

AI Capability Matrix

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

CapabilityStatusRepresentative ModelDetail
LLM Chat (7B)Needs offloadLlama 3.1 8B Q4β€”
LLM Coding (30B)Won’t fitQwen 3 30B Q4β€”
budget-friendlyoneapi-sycllimited-vramsoftware-immature

Specifications

Compute
FP169 TFLOPS
INT872 TOPS
ArchitectureAlchemist
Memory
VRAM6 GB
Bandwidth186 GB/s
General
FamilyArc A
SegmentConsumer
InterconnectPCIe 4
Compute PlatformONEAPI
MSRP$139

Key Features

Intel Xe Matrix Extensions (XMX) for INT8/FP16 acceleration6 GB GDDR6 at 186 GB/s bandwidthSYCL/oneAPI and Vulkan backend support in llama.cpp72 TOPS INT8 computePCIe Gen 4 interfaceAlchemist (Xe HPG) entry-level architecture

For AI Workloads

Strengths
  • Very affordable entry point to discrete GPU inference β€” often available for under $100 used
  • Provides a meaningful step up from CPU-only inference for 7B Q4 models with partial offloading
  • Vulkan backend in llama.cpp offers a simpler setup path than the full oneAPI toolchain
  • Low power draw (75W) β€” runs without auxiliary power connector on many systems
Considerations
  • 6 GB VRAM is the primary constraint β€” 7B models require CPU offloading, reducing throughput
  • 186 GB/s bandwidth is among the lowest of any dedicated GPU, making decode speed a bottleneck
  • oneAPI software ecosystem immaturity means troubleshooting takes more time than CUDA equivalents
  • Not practical for regular production LLM use; best for light experimentation and learning

Architecture

Alchemist

Alchemist is Intel's first discrete GPU architecture under the Arc brand, using Xe-HPG cores manufactured on TSMC's N6 process. It features XMX (Xe Matrix Extensions) engines for AI acceleration.

AI Relevance

XMX engines provide some AI inference acceleration via oneAPI/SYCL. However, the software ecosystem for LLM inference on Intel Arc is still developing, with limited runtime support compared to CUDA.

Process: TSMC N6Platform: ONEAPIPrecisions: FP32, FP16, INT8

Recommendations by Workload

Chat

A

Gemma 4 E2B

Gemma 4 E2B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 24.1 tok/s Β· 42K ctx Β· llama.cppEST.
4.9 GB / 6.0 GB VRAM

Coding

A

Gemma 4 E2B

Gemma 4 E2B is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 24.1 tok/s Β· 42K ctx Β· llama.cppEST.
5.1 GB / 6.0 GB VRAM

Agentic Coding

A

Full Model Compatibility

πŸ‘ Alibaba
Qwen 3.5 4B
S90
4B6.1 GB29 tok/s15K ctx
dense
πŸ‘ Microsoft
Phi-4 Mini Reasoning 4B
S88
3.8B5.3 GB42 tok/s24K ctx
dense
πŸ‘ Jina AI
Jina Embeddings v3
S86

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

18 of 52 models can generate images or video on your Intel Arc A380 6GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512Γ—512~7.1sA
Stable Diffusion 1.5Image512Γ—768~14.1sB
Realistic Vision v5.1Image512Γ—768~14.1sB
DreamShaper 8Image512Γ—768~14.1sB
LCM DreamShaper v7

Upgrade paths

Upgrade from Intel Arc A380 6GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

πŸ‘ NVIDIA
RTX 3050 8GBNext step up
8 GB VRAM (+2)224 GB/s (+38)
B
Unlocks 38 additional models that do not fit on the current setup.Unlocks Qwen 3.5 9B, Qwen 3 8B, Nemotron Nano 8B+35 more Β· +31% faster avg

Unlocks 38 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 31%.

~$249 MSRP

πŸ‘ Intel
Intel Arc A550M 8GBIntel upgrade
8 GB VRAM (+2)224 GB/s (+38)
B
Unlocks 38 additional models that do not fit on the current setup.Unlocks Qwen 3.5 9B, Qwen 3 8B, Nemotron Nano 8B+35 more Β· +27% faster avg

Unlocks 38 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 27%.

RX 7600 XT 16GBBest value
16 GB VRAM (+10)288 GB/s (+102)
A
Unlocks 112 additional models that do not fit on the current setup.Unlocks Qwen 3.5 9B, Magistral Small 2507, Devstral Small 2 24B Instruct+109 more Β· +49% faster avg

Unlocks 112 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 49%.

~$329 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+282)8000 GB/s (+7814)
B
Unlocks 193 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+190 more Β· +671% faster avg

Unlocks 193 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 671%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

Intel Arc A380 6GB vs RTX 2060 6GBIntel Arc A380 6GB vs RTX 4050 Laptop 6GBIntel Arc A380 6GB vs Intel Arc Pro A40 6GB
Compare this GPUCompare with another GPU β†’
6GB
VRAM
186GB/s
Bandwidth
9TFLOPS
FP16 Compute
72TOPS
INT8 Inference
$139 MSRP
Intel Arc A380 6GBCategory AvgRTX 3050 8GB
LLM Large (70B)
Won’t fit
Llama 3.1 70B Q4
β€”
Image Gen (SDXL)Very constrainedSDXL 1.0 FP16~~56.5s per image
Image Gen (Flux)Won't fitFlux.1 Dev FP16~~4m 14s per image
Image Gen (SD 3.5)Won't fitSD 3.5 Large FP16~~5m 11s per image
Video Short (25f)Won't fitLTX Video 2B~~49.1s/frame
Video Long (100f)Won't fitWan Video 14B~~2m 25s/frame

Gemma 4 E2B

Gemma 4 E2B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 24.1 tok/s Β· 42K ctx Β· llama.cppEST.
5.7 GB / 6.0 GB VRAM

Reasoning

A

Gemma 4 E2B

Gemma 4 E2B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 24.1 tok/s Β· 42K ctx Β· llama.cppEST.
5.1 GB / 6.0 GB VRAM

RAG

A

Ministral 3 3B

Ministral 3 3B is viable for RAG, but is not the most specialized choice. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface.

Decode 42.0 tok/s Β· 58K ctx Β· llama.cppEST.
4.8 GB / 6.0 GB VRAM
0.57B4.6 GB8 tok/s8K ctx
dense
πŸ‘ BAAI
BGE M3
A84
0.57B3.8 GB8 tok/s8K ctx
dense
πŸ‘ Alibaba
Qwen3-Coder 30B A3B Instruct
F0
30.5B21.6 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 397B A17B
F0
397B246.5 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Devstral 2 123B Instruct
F0
123B81.9 GB2 tok/s4K ctx
dense
πŸ‘ Moonshot AI
Kimi K2.5
F0
1000B618.9 GB2 tok/s4K ctx
moe
πŸ‘ Moonshot AI
Kimi K2.6
F0
1000B618.9 GB2 tok/s4K ctx
+1moe
πŸ‘ DeepSeek
DeepSeek V4 Pro
F0
1600B865.4 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 27B
F0
27B21.1 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3.6 27B
F0
27B18.9 GB2 tok/s4K ctx
+1dense
πŸ‘ Alibaba
Qwen 3.5 122B A10B
F0
122B78.4 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen3-VL 30B A3B Instruct
F0
30B21.3 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.6 35B A3B
F0
35B27.0 GB2 tok/s4K ctx
+1moe
πŸ‘ DeepSeek
DeepSeek V4 Flash
F0
284B160.8 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 9B
F0
9B9.2 GB6 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3.5 35B A3B
F0
35B24.3 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Magistral Small 2507
F0
24B18.6 GB2 tok/s4K ctx
dense
πŸ‘ Mistral
Devstral Small 2 24B Instruct
F0
24B18.6 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 32B
F0
32B24.9 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 14B
F0
14B12.5 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 30B A3B
F0
30.5B21.6 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Mistral Small 4 119B
F0
119B79.5 GB2 tok/s4K ctx
moe
πŸ‘ Cohere
Command A 111B
F0
111B73.1 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 2.5 VL 72B
F0
72B50.3 GB2 tok/s4K ctx
dense
πŸ‘ OpenAI
GPT-OSS 120B
F0
117B77.8 GB2 tok/s4K ctx
dense
πŸ‘ NVIDIA
Nemotron 3 Nano 30B
F0
30B22.2 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 8B
F0
8B8.6 GB7 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen3-Coder-Next
F0
80B51.8 GB2 tok/s4K ctx
moe
πŸ‘ Microsoft
Phi-4-reasoning-plus 14B
F0
14.7B13.5 GB2 tok/s4K ctx
dense
πŸ‘ Mistral
Devstral Small 1.1
F0
24B18.6 GB2 tok/s4K ctx
dense
πŸ‘ Z.ai
GLM-5.1
F0
754B480.5 GB2 tok/s4K ctx
moe
πŸ‘ Mistral AI
Pixtral Large 124B
F0
124B82.5 GB2 tok/s4K ctx
dense
πŸ‘ Z.ai
GLM-5
F0
744B474.4 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.2
F0
671B411.3 GB2 tok/s4K ctx
moe
πŸ‘ OpenAI
GPT-OSS 20B
F0
21B16.8 GB3 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3 235B A22B
F0
235B147.7 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B297.2 GB2 tok/s4K ctx
moe
πŸ‘ NVIDIA
Nemotron Cascade 2 30B A3B
F0
30B22.7 GB2 tok/s4K ctx
moe
πŸ‘ Google
Gemma 4 31B
F0
30.7B34.9 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B145.6 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Leanstral 119B A6B
F0
119B82.9 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek Coder V2 236B
F0
236B204.1 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek R1 671B
F0
671B470.4 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.1 671B
F0
671B470.4 GB2 tok/s4K ctx
moe
πŸ‘ NVIDIA
Nemotron Nano 8B
F0
8B8.3 GB8 tok/s4K ctx
dense
πŸ‘ Mistral
Ministral 3 14B
F0
14B12.5 GB2 tok/s4K ctx
multimodal
πŸ‘ LG AI
EXAONE 4.0 32B
F0
32B24.9 GB2 tok/s4K ctx
dense
πŸ‘ Google
Gemma 4 26B A4B
F0
25.2B20.5 GB2 tok/s4K ctx
moe
Image
512Γ—768
~4.2s
B
PixArt-SigmaImage256Γ—256~56.5sB
FramePack I2VVideo256Γ—256~1m 44s/frameB
SDXL TurboImage256Γ—256~7.1sD
SDXL LightningImage256Γ—256~21.2sD
Stable Diffusion XL 1.0Image256Γ—256~56.5sD
Playground v2.5Image256Γ—256~1m 25sD
RealVisXL v5.0Image256Γ—256~1m 4sD
DreamShaper XLImage256Γ—256~1m 4sD
Juggernaut XL v9Image256Γ—256~1m 4sD
Animagine XL 3.1Image256Γ—256~1m 4sD
Pony Diffusion V6 XLImage256Γ—256~1m 4sD
Animagine XL 4.0Image256Γ—256~1m 4sD
Illustrious XLImage256Γ—256~1m 4sD
Wan Video 2.1 1.3BVideo256Γ—256~41.3s/frameF
Stable Diffusion 3.5 MediumImage256Γ—256~1m 39sF
Flux.2 Klein 4BImage256Γ—256~17sF
LTX Video 2BVideo256Γ—256~49.1s/frameF
KolorsImage256Γ—256~1m 53sF
Stable CascadeImage256Γ—256~2m 21sF
AuraFlow v0.3Image256Γ—256~4m 14sF
Stable Diffusion 3.5 LargeImage256Γ—256~5m 11sF
Stable Diffusion 3.5 Large TurboImage256Γ—256~56.5sF
CogVideoX 2BVideo256Γ—256~49.1s/frameF
HunyuanVideoVideo256Γ—256~1m 44s/frameF
ChromaImage256Γ—256~56.5sF
Z-Image TurboImage256Γ—256~58.3sF
Flux.1 DevImage256Γ—256~4m 14sF
Flux.1 SchnellImage256Γ—256~49.5sF
LTX Video 13BVideo256Γ—256~1m 44s/frameF
Flux.1 Kontext DevImage256Γ—256~4m 43sF
AnimateDiff v1.5.3Video512Γ—768~25.8s/frameF
Cosmos Diffusion 7BVideo256Γ—256~1m 21s/frameF
CogVideoX 5BVideo256Γ—256~1m 11s/frameF
Wan2.2 TI2V 5BVideo256Γ—256~1m 11s/frameF
Flux.2 Klein 9BImage256Γ—256~28.3sF
Flux.1 Fill DevImage256Γ—256~4m 0sF
Mochi 1 PreviewVideo256Γ—256~1m 33s/frameF
HunyuanVideo 1.5Video256Γ—256~1m 27s/frameF
Helios 14BVideo256Γ—256~1m 47s/frameF
SkyReels V2 14BVideo256Γ—256~1m 47s/frameF
Wan Video 2.1 14BVideo256Γ—256~1m 47s/frameF
Wan Video 2.2 14BVideo256Γ—256~1m 47s/frameF
Qwen ImageImage256Γ—256~1m 35sF
Qwen Image EditImage256Γ—256~1m 35sF
Flux.2 DevImage256Γ—256~44m 34sF
MAGI-1Video256Γ—256~2m 13s/frameF
HunyuanImage 3.0Image256Γ—256~2m 48sF

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

Buying advice

Should you buy Intel Arc A380 6GB for local AI?

Usable for local AI with limits

Can run 4 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.

6.0 GB

VRAM

$139

MSRP

$23/GB

Cost per GB VRAM

Best models for this GPU

What will limit you first

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best upgrade itinerary

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Unlocks 38 additional models that do not fit on the current setup.

Want more headroom? RTX 3050 8GB (8.0 GB VRAM) is the next step up.