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All estimates are approximations based on mathematical models and public specifications. Actual performance may vary. Do not make purchasing decisions based solely on these estimates.

Data sourced from Hugging Face, Ollama, and official model documentation. Model names and logos are trademarks of their respective owners.

© 2026 Will It Run AI — Fase Consulting Ibiza, S.L. (NIF: B57969656)

Home/Hardware/GPUs/Intel Arc B570 10GB
Intel

Intel

Intel Arc B570 10GB

Arc BConsumerBattlemagePCIe 5oneAPI
10GB
VRAM
380GB/s
Bandwidth
19TFLOPS
FP16 Compute
152TOPS
INT8 Inference
VRAM10 GBBandwidth380 GB/sCompute19 TFInference152 TOPS
Intel Arc B570 10GBCategory AvgGTX 1080 Ti 11GB

Specifications

Compute
FP1619 TFLOPS
INT8152 TOPS
ArchitectureBattlemage
Memory
VRAM10 GB
Bandwidth380 GB/s
General
FamilyArc B
SegmentConsumer
InterconnectPCIe 5
Compute PlatformONEAPI

Architecture

Battlemage

Battlemage is Intel's second-generation Arc GPU architecture (Xe2-HPG), built on TSMC N4. It delivers significant performance-per-watt improvements over Alchemist with enhanced XMX engines and improved driver maturity.

AI Relevance

Better driver stability and improved XMX throughput make Battlemage more viable for AI inference than Alchemist. The Arc B580 (12 GB) is an increasingly popular budget option for local LLM experimentation via SYCL/oneAPI backends in llama.cpp.

Process: TSMC N4Platform: ONEAPIPrecisions: FP32, FP16, BF16, INT8

Recommendations by Workload

Agentic Coding

C

Codestral Mamba 7B

This model is still usable for agentic-coding, but it is not the most specialized pick. It sits in the middle of the current model mix. It should run, but memory headroom will be limited. Known channels: huggingface, ollama.

Decode 48.1 tok/s · 38K ctx · llama.cpp
8.4 GB / 10.0 GB VRAM

Chat

C

Qwen 3 8B

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 42.0 tok/s · 11K ctx · llama.cpp
7.6 GB / 10.0 GB VRAM

Coding

C

Codestral Mamba 7B

This model is still usable for coding, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.

Decode 48.1 tok/s · 22K ctx · llama.cpp
7.3 GB / 10.0 GB VRAM

RAG

C

granite 8b code instruct 4k

This model is a direct match for rag. It sits in the middle of the current model mix. It should run, but memory headroom will be limited.

Decode 42.0 tok/s · 34K ctx · llama.cpp
9.3 GB / 10.0 GB VRAM

Reasoning

C

Qwen 3 8B

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 42.0 tok/s · 20K ctx · llama.cpp
8.0 GB / 10.0 GB VRAM

Full Model Compatibility

TheBlokeTLlama 2 7B Chat
B55
7B7.3 GB48 tok/s22K ctx
dense
TheBlokeTMistral 7B Instruct v0.2
B55
7B7.3 GB48 tok/s22K ctx
dense
MaziyarPanahiMMistral 7B Instruct v0.3
B55
7B7.3 GB48 tok/s22K ctx
dense
BartowskiBMeta Llama 3.1 8B Instruct
C55
8B8.0 GB42 tok/s20K ctx
dense
XtunerXllava llama 3 8b v1 1
C55
8B8.0 GB42 tok/s20K ctx
dense
UnslothDeepSeek R1 0528 Qwen3 8B
C55
8B8.0 GB42 tok/s20K ctx
dense
MaziyarPanahiMMeta Llama 3 8B Instruct
C55
8B8.0 GB42 tok/s20K ctx
dense
UnslothQwen3.5 4B
C53
4B5.1 GB84 tok/s31K ctx
dense
Lmstudio-communityLgemma 3 4b it
C53
4B5.1 GB84 tok/s31K ctx
dense
BartowskiBLlama 3.2 3B Instruct
C53
3B4.9 GB97 tok/s33K ctx
dense
QwenQwen2.5 3B Instruct
C52
3B4.5 GB112 tok/s35K ctx
dense
BartowskiBgemma 2 2b it
C51
2B4.3 GB131 tok/s37K ctx
dense
UnslothQwen3.5 9B
C51
9B8.8 GB37 tok/s18K ctx
dense
HauhauCSHQwen3.5 9B Uncensored HauhauCS Aggressive
C51
9B8.8 GB37 tok/s18K ctx
dense
Lmstudio-communityLQwen3.5 9B
C51
9B8.8 GB37 tok/s18K ctx
dense
Googlegemma 2b
C51
2B3.9 GB168 tok/s41K ctx
dense
TheDrummerTGemmasutra Mini 2B v1
C50
2B3.9 GB168 tok/s41K ctx
dense
QwenQwen2.5 1.5B Instruct
C50
1.5B3.6 GB205 tok/s44K ctx
dense
Hugging-quantsHLlama 3.2 1B Instruct Q8 0
C50
1B3.5 GB216 tok/s45K ctx
dense
TheBlokeTTinyLlama 1.1B Chat v1.0
C49
1.1B3.4 GB205 tok/s47K ctx
dense
Ggml-orgGSmolVLM 500M Instruct
C49
0.5B3.1 GB216 tok/s51K ctx
dense
Ggml-orgGembeddinggemma 300M
C48
0.3B2.9 GB216 tok/s54K ctx
dense
DeepSeekDeepSeek R1 671B
F0
671B417.0 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B96.1 GB3 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B462.0 GB2 tok/s4K ctx
moe
UnslothQwen3.5 27B
F0
27B22.6 GB13 tok/s7K ctx
dense
UnslothQwen3.5 35B A3B
F0
35B28.7 GB10 tok/s6K ctx
dense
Moonshot AIKimi K2.5
F0
1000B616.9 GB2 tok/s4K ctx
moe
MistralMistral Large 3
F0
675B420.1 GB2 tok/s4K ctx
+1moe
MistralMistral Small 4 119B
F0
119B75.5 GB8 tok/s4K ctx
moe
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.3 GB29 tok/s8K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B300.2 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder-Next
F0
80B51.5 GB13 tok/s4K ctx
moe
UnslothQwen3.5 122B A10B
F0
122B80.7 GB3 tok/s4K ctx
dense
DeepSeekDeepSeek V3 671B
F0
671B417.0 GB2 tok/s4K ctx
moe
MistralMixtral 8x22B
F0
141B94.0 GB5 tok/s4K ctx
moe
AlibabaQwen 2.5 72B
F0
72B57.1 GB5 tok/s4K ctx
dense
AlibabaQwen 3 235B A22B
F0
235B148.7 GB4 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B21.0 GB30 tok/s8K ctx
moe
UnslothQwen3.5 397B A17B
F0
397B306.1 GB2 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B20.3 GB14 tok/s8K ctx
dense
MetaLlama 3.3 70B
F0
70B55.5 GB5 tok/s4K ctx
dense
MetaLlama 4 Maverick 17B 128E
F0
400B248.6 GB3 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B87.0 GB3 tok/s4K ctx
dense
AlibabaQwen 2.5 Coder 32B
F0
32B26.4 GB11 tok/s6K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B57.1 GB5 tok/s4K ctx
dense
Unslothgemma 3 27b it
F0
27B22.6 GB13 tok/s7K ctx
dense
Lmstudio-communityLQwen3.5 35B A3B
F0
35B28.7 GB10 tok/s6K ctx
dense
MistralCodestral 2 25.08
F0
22B18.8 GB15 tok/s9K ctx
dense
MistralDevstral Small 1.1
F0
24B20.3 GB14 tok/s8K ctx
dense

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

DeepSeekDeepSeek R1 671B
671BTier 5Needs ~422.8 GB
MistralDevstral 2 123B Instruct
123BTier 5Needs ~115.4 GB
Runs on Mac Studio M3 Ultra 256GB
Z.aiGLM-5
744BTier 5Needs ~468.2 GB
UnslothQwen3.5 27B
27BTier 5Needs ~26.8 GB
Runs on RTX 5090 32GB (~$1,999)
UnslothQwen3.5 35B A3B
35BTier 5Needs ~34.2 GB
Runs on Mac mini M4 64GB (~$1,099)

Upgrade paths

Upgrade from Intel Arc B570 10GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

NVIDIAGTX 1080 Ti 11GBNext step up
11 GB VRAM (+1)484 GB/s (+104)
A
Unlocks gemma 3 12b it, Llama 3.2 11B Vision, DeepSeek Coder V2 16B+3 more · +37% faster avg

 

IntelIntel Arc B580 12GBIntel upgrade
12 GB VRAM (+2)456 GB/s (+76)
A
Unlocks gemma 3 12b it, Llama 3.2 11B Vision, DeepSeek Coder V2 16B+8 more · +3% faster avg

~$249 MSRP

AMDRX 7600 XT 16GBBest value
16 GB VRAM (+6)
A
Unlocks StarCoder 15B, DeepSeek R1 Distill Qwen 14B, Phi-4-reasoning-plus 14B+34 more

~$329 MSRP

AMDAMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+278)8000 GB/s (+7620)
A
Unlocks Devstral 2 123B Instruct, Qwen3.5 27B, Qwen3.5 35B A3B+118 more · +1872% faster avg

~$8,000 MSRP

Compare this GPU