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What local AI models can the Intel Arc Pro B70 run?

79 of our 115 models, up to Recursive Flint 40B-A2.5B. That is the largest current model that fits the Arc Pro B70's 32 GB at Q4_1 with room for about 168K tokens of context (its full 256K with 8-bit KV cache), at about 144 tok/s estimate.

Memory32 GB GDDR6source
Bandwidth608 GB/ssource
Models that fit79 of 115
PriceNo price yet
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Memory type: GDDR6 (source) · Bus width: 256-bit (source) · Released: 2026 (source)

Specs from first-party pages (Intel), accessed 2026-10-18. Speeds use the Intel (Vulkan) constants (how).

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Every model, largest that fits first

ModelQuantFileFits?Speed estimateMax context estimate
Recursive Flint 40B-A2.5BQ4_125.99 GBFits~144 tok/s168K · 256K (8-bit)
NVIDIA Nemotron 3.5 Lightning 30B-A3BQ4_K_M25.48 GBFits~87 tok/s256K (full)
K2-Horizon-MoVA-36B-A4BQ4_K_M22.37 GBFits~110 tok/s42K · 80K (8-bit)
Qwen3.6-35B-A3BQ4_K_M22.29 GBFits~100 tok/s256K (full)
Qwen3.5-35B-A3BQ4_K_M22.29 GBFits~100 tok/s256K (full)
NVIDIA Nemotron 3.5 Bolt 74B-A9B 24GB (2-bit)native 2-bit22.02 GBFits~82 tok/s256K (full)
MiniCPM-V 4.7 35B-A3BQ4_K_M21.17 GBFits~102 tok/s256K (full)
K2-Horizon-32BQ4_K_M21.08 GBFits~17 tok/s36K · 69K (8-bit)
Nanbeige4.3-33BQ4_K_M20.79 GBFits~17 tok/s46K · 88K (8-bit)
Xing4.0-29B-A4BQ4_K_M20.06 GBFits~105 tok/s231K · 256K (8-bit)
Raon-1 mini preview Early supportQ4_K_M19.69 GBWeights fit context unknown~77 tok/sUnknown (no KV data)
PLaMo 3 Translate 31B Translation onlyQ4_K_M19.53 GBFits~18 tok/s32K (full)
Meta Muse Glimmer 30BQ4_K_M18.39 GBFits~20 tok/s128K (full)
Gemma 4 31BQ4_K_M18.32 GBFits~20 tok/s136K · 256K (8-bit)
GLM-4.7-FlashQ4_K_M18.31 GBFits~137 tok/s198K (full)
Qwen3.6-27BQ4_K_M17.98 GBFits~19 tok/s194K · 256K (8-bit)
Qwen3.5-27BQ4_K_M17.98 GBFits~19 tok/s194K · 256K (8-bit)
IBM Granite 4.2 30BQ4_K_M17.72 GBFits~20 tok/s49K · 93K (8-bit)
Microsoft Fara1.5 27BQ4_K_M17.53 GBFits~20 tok/s200K · 256K (8-bit)
Qwen3.8-27BQ4_K_M17.44 GBFits~20 tok/s202K · 256K (8-bit)
Gemma 4 26B A4BQ4_K_M17.04 GBFits~106 tok/s256K (full)
Kvark Sparkle 1.1 27BQ4_K_M16.81 GBFits~20 tok/s211K · 256K (8-bit)
LFM2.5-24B-A2BQ4_K_M14.53 GBFits~122 tok/s128K (full)
Mistral-Small-3.2-24B-Instruct-2506Q4_K_M14.33 GBFits~25 tok/s99K · 128K (8-bit)
Devstral-Small-2505Q4_K_M14.33 GBFits~25 tok/s99K · 128K (8-bit)
gpt-oss-20bQ4_K_M11.67 GBFits~125 tok/s128K (full)
phi-4Q4_K_M9.05 GBFits~40 tok/s16K (full)
JetBrains Mellum2.1 12B-A2.5B ThinkingQ4_K_M8.07 GBFits~142 tok/s128K (full)
Gemma 4 12BQ4_K_M7.12 GBFits~50 tok/s256K (full)
NVIDIA Nemotron 3.5 Bolt E4BQ4_K_M6.18 GBFits~58 tok/s64K (full)
Qwen3.5-9BQ4_K_M6.17 GBFits~51 tok/s256K (full)
Ternary Bonsai 2 27BPTQ1_0 ternary5.95 GBFits~53 tok/s256K (full)
Microsoft Fara1.5 9BQ4_K_M5.91 GBFits~53 tok/s256K (full)
Xiaomi MiMo-V2.6-Distill-Qwen-9BQ4_K_M5.84 GBFits~54 tok/s256K (full)
ZoomGain-9BQ4_K_M5.63 GBFits~55 tok/s256K (full)
K2-Horizon-7BQ4_K_M5.59 GBFits~64 tok/s168K · 316K (8-bit)
webAI TwIL-8B Math and code reasoningQ4_K_M5.35 GBFits~67 tok/s128K (full)
IBM Granite 4.2 8BQ4_K_M5.35 GBFits~67 tok/s128K (full)
PLaMo 3 Translate 8B Translation onlyQ4_K_M5.28 GBFits~68 tok/s32K (full)
LFM2.5-8B-A1BQ4_K_M5.16 GBFits~136 tok/s128K (full)
Gemma 4 E4BQ4_K_M4.98 GBFits~72 tok/s128K (full)
Spark-X2.6-4BQ4_K_M3.54 GBFits~101 tok/s724K · 1M (8-bit)
Gemma 4 E2BQ4_K_M3.46 GBFits~104 tok/s128K (full)
Qwen3.5-4BQ4_K_M3.01 GBFits~93 tok/s256K (full)
NVIDIA Nemotron 3 Nano 4BQ4_K_M2.9 GBFits~95 tok/s256K (full)
Microsoft Fara1.5 4BQ4_K_M2.88 GBFits~96 tok/s256K (full)
ZoomGain-4BQ4_K_M2.71 GBFits~100 tok/s256K (full)
IBM Granite 4.2 3BQ4_K_M2.24 GBFits~160 tok/s128K (full)
Raon-1 nano Early supportQ4_K_M2.08 GBWeights fit context unknown~122 tok/sUnknown (no KV data)
Ternary Bonsai 2 9BPTQ1_0 ternary2 GBFits~125 tok/s256K (full)
PLaMo 3 Translate 2B Translation onlyQ4_K_M1.81 GBFits~198 tok/s32K (full)
LFM3-1.7BQ4_K_M1.1 GBFits~183 tok/s256K (full)
LFM3-VL-2BQ4_K_M1.1 GBFits~183 tok/s128K (full)
Ternary Bonsai 2 4BPTQ1_0 ternary0.95 GBFits~198 tok/s256K (full)
MiniCPM-V 4.7 1BQ4_K_M0.67 GBFits~234 tok/s256K (full)
PLaMo 3 Translate 0.8B Translation onlyQ4_K_M0.57 GBFits~629 tok/s32K (full)
BotPsy-0.8BQ4_K_M0.53 GBFits~258 tok/s256K (full)
Ternary Bonsai 2 2BPTQ1_0 ternary0.43 GBFits~278 tok/s256K (full)
Qwen2.5-Coder-32B-Instruct OlderQ4_K_M19.85 GBFits~18 tok/s32K (full)
DeepSeek-R1-Distill-Qwen-32B OlderQ4_K_M19.85 GBFits~18 tok/s41K · 78K (8-bit)
Qwen3-32B OlderQ4_K_M19.76 GBFits~18 tok/s40K (full)
Qwen3-30B-A3B OlderQ4_K_M18.63 GBFits~125 tok/s40K (full)
Qwen3-Coder-30B-A3B-Instruct OlderQ4_K_M18.56 GBFits~125 tok/s123K · 232K (8-bit)
gemma-3-27b-it OlderQ4_K_M16.55 GBFits~22 tok/s128K (full)
LFM2-24B-A2B OlderQ4_K_M14.42 GBFits~122 tok/s128K (full)
Qwen3-14B OlderQ4_K_M9 GBFits~40 tok/s40K (full)
Qwen2.5-Coder-14B-Instruct OlderQ4_K_M8.99 GBFits~40 tok/s32K (full)
DeepSeek-R1-Distill-Qwen-14B OlderQ4_K_M8.99 GBFits~40 tok/s109K · 128K (8-bit)
Mistral-Nemo-Instruct-2407 OlderQ4_K_M7.48 GBFits~48 tok/s128K (full)
gemma-3-12b-it OlderQ4_K_M7.3 GBFits~49 tok/s128K (full)
NVIDIA-Nemotron-Nano-9B-v2 OlderQ4_K_M6.53 GBFits~49 tok/s128K (full)
Qwen3-8B OlderQ4_K_M5.03 GBFits~71 tok/s40K (full)
Llama-3.1-8B-Instruct OlderQ4_K_M4.92 GBFits~73 tok/s128K (full)
Qwen2.5-Coder-7B-Instruct OlderQ4_K_M4.68 GBFits~77 tok/s32K (full)
Mistral-7B-Instruct-v0.3 OlderQ4_K_M4.37 GBFits~82 tok/s32K (full)
Qwen3-4B OlderQ4_K_M2.5 GBFits~143 tok/s40K (full)
gemma-3-4b-it OlderQ4_K_M2.49 GBFits~144 tok/s128K (full)
Llama-3.2-3B-Instruct OlderQ4_K_M2.02 GBFits~178 tok/s128K (full)
Qwen3-0.6B OlderQ4_K_M0.48 GBFits~747 tok/s40K (full)
Ripple-65B PreviewQ4_K_M38.8 GBNo——
Llama-3.3-70B-Instruct OlderQ4_K_M42.52 GBNo——
DeepSeek-R1-Distill-Llama-70B OlderQ4_K_M42.52 GBNo——
Qwen2.5-72B-Instruct OlderQ4_K_M47.42 GBNo——
Qwen3-Coder-NextQ4_K_M48.53 GBNo——
Aleph Alpha Kolibri-1 78B-A3.5BQ4_K_M49.76 GBNo——
NVIDIA Nemotron 3.5 Bolt 74B-A9BUD-Q4_K_XL50.22 GBNo——
gpt-oss-120bQ4_K_M62.84 GBNo——
NVIDIA Nemotron 3.5 Super VL 120B-A12BQ4_K_M70.1 GBNo——
Mistral Small 4 119BQ4_K_M72.64 GBNo——
GLM-4.5-Air OlderQ4_K_M73.5 GBNo——
Qwen3.5-122B-A10BQ4_K_M77.62 GBNo——
Mistral Medium 3.5 128BQ4_K_M78.41 GBNo——
Ornith-1.5-122BQ4_K_M78.92 GBNo——
NVIDIA Nemotron 3 Super 120B-A12B OlderUD-Q4_K_M82.54 GBNo——
Swift 1.5 Qwen3.8-Flash-NextQ4_K_M119.6 GBNo——
Qwen3.8-Flash-NextQ4_K_M119.6 GBNo——
StepFun Step 3.7 FlashUD-Q4_K_M122.09 GBNo——
Qwen3-235B-A22BQ4_K_M142.15 GBNo——
MiniMax-M3.1-Flash Unsloth 2-bitUD-Q2_K_XL143.91 GBNo——
DeepSeek-V4-FlashUD-Q4_K_XL155.1 GBNo——
MiniMax-M3.1-Flash Q2_KQ2_K155.91 GBNo——
Thinking Machines Inkling-Small OlderQ4_K_M159.34 GBNo——
Xiaomi MiMo-V2.6-Flash 309B-A15BMXFP4167.37 GBNo——
GLM-5.3-FlashUD-Q4_K_XL199.71 GBNo——
MiniMax-M3.1-FlashQ4_K_M258.01 GBNo——
Reflection Beam 1.0 501B-A23BQ4_K_M302.41 GBNo——
Thinking Machines Inkling 2 Small Early supportUD-Q4_K_XL354.15 GBNo——
inclusionAI Ling-3.1-flash 560B-A25BQ4_K_M357.97 GBNo——
DeepSeek-V4.1-Flash (1017)UD-Q4_K_XL500.71 GBNo——
Meta Muse Spark Open 816B-A42BUD-Q4_K_XL505.5 GBNo——
Mistral Large 4UD-Q4_K_XL605 GBNo——

Q4_K_M unless the row says otherwise; UD- sizes are unsloth dynamic quants, the only Q4 build published for those models. Max context is an estimate of the longest context whose KV cache fits beside the model: default FP16 cache, then 8-bit (q8_0).

Speed calculator

estimate

Estimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works

Reported by others

These are speed reports posted by individual users or reviewers. We haven't checked or reproduced them. They are not our estimates, they don't feed the estimator or My Rig, and they can't be compared with the tok/s figures elsewhere on this site or with each other, because each used a different model, quant, engine and test.

What this does and doesn't show

For coding agents, how much total output a machine keeps up with several requests at once, and how long each waits for its first token, matter as much as single-request tok/s. These reports show that for specific setups only; they don't show what you'd get with a different engine, quant or workload.

Qwen3.8-27B Q6 on one Arc Pro B70: what each software change did

Reported by Reddit user on r/LocalLLaMA (summarized by @wei_wang on X)
How this run was set up
Model & revisionQwen3.8-27B; exact GGUF file and revision Unknown
QuantQ6 (exact file Unknown)
Runtime & versionSteps 1–3: Ollama on Vulkan (Mesa 25.2, then Mesa 26.2). Steps 4–5: llama.cpp llama-server built with SYCL (Intel oneAPI); llama.cpp version Unknown
Topology1x Intel Arc Pro B70 (32 GB); kernel 6.17 or newer (Ubuntu 7.0 HWE kernel), Resizable BAR on, Intel compute runtime from a PPA
Input / output / contextFigures from the author's 16k-context column; output length Unknown
ConcurrencyUnknown
Speculative decodingOff in steps 1–4. On only in step 5: MTP with up to 4 draft tokens (--spec-type draft-mtp --spec-draft-n-max 4)
How speed was timedUnknown
Date measuredUnknown (summary posted 2026-11-10)
SourceOriginal Reddit post (r/LocalLLaMA) · @wei_wang on X, 2026-11-10
Evidence statusSelf-reported, not independently reproduced

Plain decoding (no drafting), generation at 16k context

WhatReportedNote
1. Ollama + Mesa 25.26.8 tok/s—
2. Mesa 26.219.4 tok/s—
3. Mesa 26.2, f16 KV cache33 tok/s—
4. llama.cpp SYCL, f16 KV cache49 tok/s—

With speculative decoding (MTP, up to 4 drafts)

WhatReportedNote
5. llama.cpp SYCL, q8_0 KV cache, MTP56.5 tok/s—

This page's own figure for Qwen3.8-27B is an estimate for Q4_K_M and is unchanged. This ladder used Q6 and different engines, so it isn't a check of that estimate.

The author found that on Vulkan any quantized KV cache slowed generation a lot, so steps 2–3 keep f16 there. Ollama has no SYCL backend.

Caveats

  • One person's setup on one card.
  • Self-reported, not independently reproduced.
  • Step 5 uses drafting; compare steps 1–4 with each other, not with step 5.