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.
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.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | β |
| LLM Coding (30B) | Wonβt fit | Qwen 3 30B Q4 | β |
| 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
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
$213
Same tokens on cloud API
Break-even: pays for itself in 1.7 months vs cloud API at this workload. Price reference: $349 MSRP.
Recommendations by Workload
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.
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.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.8 GB
397BTier 100Needs ~246.1 GB
123BTier 100Needs ~80.2 GB
1000BTier 100Needs ~616.2 GB
1000BTier 100Needs ~616.2 GB
Image & Video Generation
Diffusion Model Compatibility
31 of 52 models can generate images or video on your Intel Arc A770 16GB
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)
CUnlocks 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
24 GB VRAM (+8)
AUnlocks 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
128 GB VRAM (+112)3700 GB/s (+3140)
BUnlocks 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)
BUnlocks 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
Intel Arc A770 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~25.2s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~1m 53s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~6m 14s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~21.9s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~1m 4s/frame |
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.
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.
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.
14B
13.5 GB
32 tok/s
33K ctx
Image
| MAGI-1Video | 256Γ256 | ~59.1s/frame | F |
Image models estimated at 1024Γ1024 (28 steps, FP16). Video models estimated at 768Γ512 (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.
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.