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URL: https://willitrunai.com/gpus/arc-a770-16gb

⇱ AI Models for Intel Arc A770 16GB β€” What Runs on 16GB VRAM


Intel

Intel Arc A770 16GB

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 A770 16GB β†’

About this GPU for AI

The Arc A770 16GB is Intel's flagship Alchemist GPU and the most capable consumer Arc card for AI inference. Its 16 GB of GDDR6 β€” more than many competing cards at the same price β€” allows it to run 7B models at FP16 or 13B models at Q4 quantization entirely on-GPU. llama.cpp's SYCL backend supports it natively, and performance has improved significantly since launch with driver and oneAPI stack maturation. At roughly 37 tokens/second on LLaMA-2-7B Q4, it offers meaningful throughput for local inference at an accessible price.

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)Runs nativelyLlama 3.1 8B Q4β€”
LLM Coding (30B)Won’t fitQwen 3 30B Q4β€”
LLM Large (70B)
high-vrambudget-friendlyoneapi-syclsoftware-immature

Specifications

Compute
FP1622 TFLOPS
INT8176 TOPS
ArchitectureAlchemist
Memory
VRAM16 GB
Bandwidth560 GB/s
TypeGDDR6
General
FamilyArc A
SegmentConsumer
InterconnectPCIe 4
Compute PlatformONEAPI
MSRP$349
TDP225W

Key Features

Intel Xe Matrix Extensions (XMX) for hardware-accelerated INT8 and FP16SYCL/oneAPI backend support in llama.cpp (oneAPI 2025.0+)16 GB GDDR6 at 560 GB/s bandwidth176 TOPS INT8 computePCIe Gen 4 x16 interfaceAlchemist (Xe HPG) architecture with ray tracing support

For AI Workloads

Strengths
  • 16 GB VRAM at this price point is exceptional β€” fits 7B at FP16 and 13B at Q4
  • ~37 tokens/sec on LLaMA-2-7B Q4 is competitive for a sub-$350 GPU
  • Mature SYCL support in llama.cpp after several years of driver improvements
  • Vulkan backend provides a simpler setup path for users who want to avoid the full oneAPI toolchain
Considerations
  • oneAPI/SYCL setup is significantly more complex than CUDA β€” requires installing the Intel oneAPI Base Toolkit
  • Known initialization issues in mixed-GPU systems (e.g., iGPU + Arc A770) under WSL
  • Community and ecosystem support for Intel GPUs is much smaller than NVIDIA
  • Most AI software assumes CUDA; expect to troubleshoot compatibility on non-mainstream tools

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

Cost vs cloud API

15.5Γ— cheaper than Claude Sonnet / GPT-4o per token

Assumes 4 hours/day of active inference at 49 tok/s, Intel Arc A770 16GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).

21.3M

Tokens/month at this pace

$13.7

Monthly local cost

$213

Same tokens on cloud API

$0.645

Local $/1M tokens

Break-even: pays for itself in 1.7 months vs cloud API at this workload. Price reference: $349 MSRP.

Recommendations by Workload

Chat

S

Qwen 3.5 9B

Qwen 3.5 9B 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 38.5 tok/s Β· 45K ctx Β· llama.cppEST.
11.0 GB / 16.0 GB VRAM

Coding

S

Qwen 3.5 9B

Qwen 3.5 9B is a specialized fit for Coding. 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 38.5 tok/s Β· 45K ctx Β· llama.cppEST.
12.1 GB / 16.0 GB VRAM

Agentic Coding

S

Full Model Compatibility

πŸ‘ Alibaba
Qwen 3.5 9B
S95
9B10.2 GB49 tok/s58K ctx
dense
S93
8B9.6 GB56 tok/s63K ctx
dense
S91

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 Intel Arc A770 16GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512Γ—512~3.1sS
Stable Diffusion 1.5Image512Γ—768~6.3sS
Realistic Vision v5.1Image512Γ—768~6.3sS
DreamShaper 8Image512Γ—768~6.3sS
LCM DreamShaper v7

Upgrade paths

Upgrade from Intel Arc A770 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

πŸ‘ 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

πŸ‘ Intel
Gaudi 3 128GBIntel upgrade
128 GB VRAM (+112)3700 GB/s (+3140)
B
Unlocks 68 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Devstral 2 123B Instruct, Qwen 3.5 27B+65 more Β· +176% faster avg

Unlocks 68 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 176%.

~$15,000 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+272)8000 GB/s (+7440)
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 Β· +280% faster avg

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

Lifts average decode speed across fitting models by about 280%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

Intel Arc A770 16GB vs RTX 4060 Ti 16GBIntel Arc A770 16GB vs RTX 4070 Ti Super 16GBIntel Arc A770 16GB vs RTX 4080 Super 16GB
Compare this GPUCompare with another GPU β†’
16GB
VRAM
560GB/s
Bandwidth
22TFLOPS
FP16 Compute
176TOPS
INT8 Inference
225W TDP$349 MSRP
Intel Arc A770 16GBCategory AvgMacBook Pro M3 24GB
Won’t fit
Llama 3.1 70B Q4
β€”
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16~~25.2s per image
Image Gen (Flux)Won't fitFlux.1 Dev FP16~~1m 53s per image
Image Gen (SD 3.5)Runs with sequential offloadSD 3.5 Large FP16~~6m 14s per image
Video Short (25f)Runs nativelyLTX Video 2B~~21.9s/frame
Video Long (100f)Won't fitWan Video 14B~~1m 4s/frame

Qwen 3.5 9B

Qwen 3.5 9B is a specialized fit for Agentic Coding. 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 49.3 tok/s Β· 58K ctx Β· llama.cppEST.
12.4 GB / 16.0 GB VRAM

Reasoning

S

Qwen 3.5 9B

Qwen 3.5 9B matches Reasoning 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 38.5 tok/s Β· 45K ctx Β· llama.cppEST.
12.1 GB / 16.0 GB VRAM

RAG

A

Granite 4.1 8B

Granite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.

Decode 55.5 tok/s Β· 56K ctx Β· llama.cppEST.
12.3 GB / 16.0 GB VRAM
14B
13.5 GB
32 tok/s
33K ctx
dense
πŸ‘ Microsoft
Phi-4-reasoning-plus 14B
S90
14.7B14.5 GB30 tok/s24K ctx
dense
πŸ‘ Alibaba
Qwen 3.5 4B
S90
4B7.1 GB56 tok/s81K ctx
dense
πŸ‘ NVIDIA
Nemotron Nano 8B
S88
8B9.3 GB56 tok/s71K ctx
dense
πŸ‘ Microsoft
Phi-4 Mini Reasoning 4B
S86
3.8B6.3 GB53 tok/s122K ctx
dense
πŸ‘ Mistral
Ministral 3 14B
S85
14B13.5 GB32 tok/s33K ctx
multimodal
πŸ‘ OpenAI
GPT-OSS 20B
A79
21B17.8 GB29 tok/s5K ctx
moe
πŸ‘ Jina AI
Jina Embeddings v3
A78
0.57B5.6 GB8 tok/s8K ctx
dense
πŸ‘ BAAI
BGE M3
A77
0.57B4.8 GB8 tok/s8K ctx
dense
πŸ‘ Alibaba
Qwen3-Coder 30B A3B Instruct
F0
30.5B22.6 GB14 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 397B A17B
F0
397B247.5 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Devstral 2 123B Instruct
F0
123B82.9 GB2 tok/s4K ctx
dense
πŸ‘ Moonshot AI
Kimi K2.5
F0
1000B619.9 GB2 tok/s4K ctx
moe
πŸ‘ Moonshot AI
Kimi K2.6
F0
1000B619.9 GB2 tok/s4K ctx
+1moe
πŸ‘ DeepSeek
DeepSeek V4 Pro
F0
1600B866.4 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 27B
F0
27B22.1 GB6 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3.6 27B
F0
27B19.9 GB6 tok/s4K ctx
+1dense
πŸ‘ Alibaba
Qwen 3.5 122B A10B
F0
122B79.4 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen3-VL 30B A3B Instruct
F0
30B22.3 GB15 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.6 35B A3B
F0
35B28.0 GB7 tok/s4K ctx
+1moe
πŸ‘ DeepSeek
DeepSeek V4 Flash
F0
284B161.8 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 35B A3B
F0
35B25.3 GB10 tok/s4K ctx
moe
πŸ‘ Mistral
Magistral Small 2507
F0
24B19.6 GB9 tok/s4K ctx
dense
πŸ‘ Mistral
Devstral Small 2 24B Instruct
F0
24B19.6 GB9 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 32B
F0
32B25.9 GB4 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 3 30B A3B
F0
30.5B22.6 GB14 tok/s4K ctx
moe
πŸ‘ Mistral
Mistral Small 4 119B
F0
119B80.5 GB2 tok/s4K ctx
moe
πŸ‘ Cohere
Command A 111B
F0
111B74.1 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 2.5 VL 72B
F0
72B51.3 GB2 tok/s4K ctx
dense
πŸ‘ OpenAI
GPT-OSS 120B
F0
117B78.8 GB2 tok/s4K ctx
dense
πŸ‘ NVIDIA
Nemotron 3 Nano 30B
F0
30B23.2 GB5 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen3-Coder-Next
F0
80B52.8 GB3 tok/s4K ctx
moe
πŸ‘ Mistral
Devstral Small 1.1
F0
24B19.6 GB9 tok/s4K ctx
dense
πŸ‘ Z.ai
GLM-5.1
F0
754B481.5 GB2 tok/s4K ctx
moe
πŸ‘ Mistral AI
Pixtral Large 124B
F0
124B83.5 GB2 tok/s4K ctx
dense
πŸ‘ Z.ai
GLM-5
F0
744B475.4 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.2
F0
671B412.3 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3 235B A22B
F0
235B148.7 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B298.2 GB2 tok/s4K ctx
moe
πŸ‘ NVIDIA
Nemotron Cascade 2 30B A3B
F0
30B23.7 GB13 tok/s4K ctx
moe
πŸ‘ Google
Gemma 4 31B
F0
30.7B35.9 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B146.6 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Leanstral 119B A6B
F0
119B83.9 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek Coder V2 236B
F0
236B205.1 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek R1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.1 671B
F0
671B471.4 GB2 tok/s4K ctx
moe
πŸ‘ LG AI
EXAONE 4.0 32B
F0
32B25.9 GB4 tok/s4K ctx
dense
πŸ‘ Google
Gemma 4 26B A4B
F0
25.2B21.5 GB16 tok/s4K ctx
moe
Image
512Γ—768
~1.9s
S
PixArt-SigmaImage1024Γ—1024~25.2sS
FramePack I2VVideo256Γ—256~46.2s/frameS
SDXL TurboImage512Γ—512~3.1sS
SDXL LightningImage1024Γ—1024~9.4sS
Stable Diffusion XL 1.0Image1024Γ—1024~25.2sS
Playground v2.5Image1024Γ—1024~37.8sS
RealVisXL v5.0Image1024Γ—1024~28.3sS
DreamShaper XLImage1024Γ—1024~28.3sS
Juggernaut XL v9Image1024Γ—1024~28.3sS
Animagine XL 3.1Image1024Γ—1024~28.3sS
Pony Diffusion V6 XLImage1024Γ—1024~28.3sS
Animagine XL 4.0Image1024Γ—1024~28.3sS
Illustrious XLImage1024Γ—1024~28.3sS
Wan Video 2.1 1.3BVideo256Γ—256~18.4s/frameS
Stable Diffusion 3.5 MediumImage256Γ—256~2m 12sS
Flux.2 Klein 4BImage256Γ—256~17sS
LTX Video 2BVideo256Γ—256~21.9s/frameS
KolorsImage256Γ—256~2m 14sA
Stable CascadeImage1024Γ—1024~1m 3sB
AuraFlow v0.3Image256Γ—256~3m 44sB
Stable Diffusion 3.5 LargeImage256Γ—256~6m 14sB
Stable Diffusion 3.5 Large TurboImage256Γ—256~1m 8sB
CogVideoX 2BVideo256Γ—256~21.9s/frameD
HunyuanVideoVideo256Γ—256~46.2s/frameD
ChromaImage256Γ—256~25.2sD
Z-Image TurboImage256Γ—256~52sD
Flux.1 DevImage256Γ—256~1m 53sF
Flux.1 SchnellImage256Γ—256~22sF
LTX Video 13BVideo256Γ—256~46.2s/frameF
Flux.1 Kontext DevImage256Γ—256~2m 6sF
AnimateDiff v1.5.3Video512Γ—768~11.5s/frameF
Cosmos Diffusion 7BVideo256Γ—256~36.1s/frameF
CogVideoX 5BVideo256Γ—256~31.5s/frameF
Wan2.2 TI2V 5BVideo256Γ—256~31.5s/frameF
Flux.2 Klein 9BImage256Γ—256~12.6sF
Flux.1 Fill DevImage256Γ—256~1m 47sF
Mochi 1 PreviewVideo256Γ—256~41.6s/frameF
HunyuanVideo 1.5Video256Γ—256~38.6s/frameF
Helios 14BVideo256Γ—256~47.6s/frameF
SkyReels V2 14BVideo256Γ—256~47.6s/frameF
Wan Video 2.1 14BVideo256Γ—256~47.6s/frameF
Wan Video 2.2 14BVideo256Γ—256~47.6s/frameF
Qwen ImageImage256Γ—256~42.4sF
Qwen Image EditImage256Γ—256~42.4sF
Flux.2 DevImage256Γ—256~19m 51sF
MAGI-1Video256Γ—256~59.1s/frameF
HunyuanImage 3.0Image256Γ—256~1m 15sF

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 A770 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

$349

MSRP

$22/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.

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.

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.