Your rig

Arc B580 · 12 GB

16 GB system RAM · Battlemage · 456 GB/s · 10.6 GB usable VRAM
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Context length

KV cache at f16, 8K tokens

Quality floor
Q8_0 — near-lossless Q6_K Q5_K_M Q4_K_M — recommended Q3_K_M Q2_K — damaged
KV cache type
System RAM (GB)
Runtime
Ollamallama.cppLM Studio

3 of 5 runtimes drive this device.

Allow CPU offload Include partial-offload fits

Offloaded layers run at 60 GB/s host bandwidth — expect single-digit tokens per second.

Which one should I actually run?

Best models for your Arc B580

Updated 21 Aug 2026 5 new this week, 14 this month

What do you want to do? Each row is the best-ranked model on that use case's shortlist that fits at Q8_0 and 8K. The ordering is editorial; the sizes and speeds are computed. How the shortlists work.

RecommendationModelWhyQuant · totalTok/s est.
Best overall Gemma 4 E4B
Google · Apache 2.0
Laptop class with images and audio in. Q8_0 · 8.7 GB 35 Runs great
Best for coding Qwen3.5 4B
Alibaba · Apache 2.0
A coding agent in 3.4 GB — the 6–8 GB answer. Q8_0 · 5.5 GB 60 Runs great
Best reasoning Qwen3.5 4B
Alibaba · Apache 2.0
Step-by-step reasoning in 8 GB. Q8_0 · 5.5 GB 60 Runs great
Best for writing Gemma 4 E4B
Google · Apache 2.0
Readable prose on a laptop. Q8_0 · 8.7 GB 35 Runs great
Best vision Gemma 4 E4B
Google · Apache 2.0
Laptop vision, audio too. Q8_0 · 8.7 GB 35 Runs great
Best for agents Granite 4.1 8B
IBM · Apache 2.0
Enterprise-grade tool calling in a dense 8B, no thinking overhead. Q8_0 · 10.5 GB 32 Tight fit
Best translation Gemma 4 E4B
Google · Apache 2.0
140+ languages on a laptop. Q8_0 · 8.7 GB 35 Runs great
Fastest good model Ling 3.0 Tiny 7.9B-A1.3B New
inclusionAI · MIT
1.3B active with a latent cache on 6 of 24 layers. Q8_0 · 8.5 GB 83 Runs great
Best long context Qwen3.5 2B
Alibaba · Apache 2.0
Long documents on 4 GB. Q8_0 · 4.3 GB
at 128K context
123 Runs great

Everything that fits

Runs great

20 of 76 · fits with headroom for a longer prompt
ModelParamsQuantWeights +KV 8KTotal VRAM headroom Tok/sRuntime
Gemma 4 E4B 8.0B Q8_0 7.9 GB 0.14 GB 8.7 GB
1.9 GB free
35 Ollama Run
Ling 3.0 Tiny 7.9B-A1.3B New 7.9B MoE / 1.3B act Q8_0 7.8 GB 0.05 GB 8.5 GB
2.1 GB free
83 llama.cpp Run
DeepSeek-R1-Distill-Qwen 7B 7.62B Q8_0 7.5 GB 0.44 GB 8.6 GB
2.0 GB free
37 Ollama Run
Qwen2.5-Coder 7B 7.62B Q8_0 7.5 GB 0.44 GB 8.6 GB
2.0 GB free
37 Ollama Run
Mistral 7B Instruct v0.3 7.25B Q8_0 7.2 GB 1.00 GB 8.8 GB
1.8 GB free
38 Ollama Run
Gemma 4 E2B 5.1B Q8_0 5.0 GB 0.07 GB 5.7 GB
4.9 GB free
55 Ollama Run
Qwen3.5 4B 4.66B Q8_0 4.6 GB 0.25 GB 5.5 GB
5.1 GB free
60 Ollama Run
Gemma 3 4B 4.3B Q8_0 4.3 GB 0.30 GB 5.2 GB
5.4 GB free
65 Ollama Run
Qwen3 4B 4.02B Q8_0 4.0 GB 1.13 GB 5.7 GB
4.9 GB free
69 Ollama Run
Ministral 3 3B 3.85B Q8_0 3.8 GB 0.81 GB 5.2 GB
5.4 GB free
72 Ollama Run
Phi-4-mini 3.8B 3.84B Q8_0 3.8 GB 1.00 GB 5.4 GB
5.2 GB free
73 Ollama Run
Granite 4.1 3B 3.4B Q8_0 3.4 GB 0.63 GB 4.6 GB
6.0 GB free
82 Ollama Run
Llama 3.2 3B Instruct 3.21B Q8_0 3.2 GB 0.88 GB 4.7 GB
5.9 GB free
87 Ollama Run
LFM2.5 2.6B New 2.7B Q8_0 2.7 GB 0.13 GB 3.4 GB
7.2 GB free
103 llama.cpp Run
Qwen3.5 2B 2.27B Q8_0 2.2 GB 0.09 GB 2.9 GB
7.7 GB free
123 Ollama Run
Qwen3 1.7B 1.72B Q8_0 1.7 GB 0.88 GB 3.2 GB
7.4 GB free
162 Ollama Run
Llama 3.2 1B Instruct 1.24B Q8_0 1.2 GB 0.25 GB 2.1 GB
8.5 GB free
225 Ollama Run
Gemma 3 1B 1.0B Q8_0 1.0 GB 0.04 GB 1.6 GB
9.0 GB free
279 Ollama Run
Qwen3.5 0.8B 0.87B Q8_0 0.9 GB 0.09 GB 1.6 GB
9.0 GB free
321 Ollama Run
Qwen3 0.6B 0.6B Q8_0 0.6 GB 0.88 GB 2.1 GB
8.5 GB free
465 Ollama Run

Runs — tight

9 models · fits at 8K, drop context or quant to go longer
ModelParamsQuantWeights +KV 8KTotal VRAM headroom Tok/sRuntime
Qwen3.5 9B 9.65B Q8_0 9.5 GB 0.25 GB 10.4 GB
0.2 GB free
29 llama.cpp Run
Ornith 1.5 9B New this week 9.41B Q8_0 9.3 GB 0.25 GB 10.2 GB
0.4 GB free
30 llama.cpp Run
Ministral 3 8B 8.92B Q8_0 8.8 GB 1.06 GB 10.5 GB
0.1 GB free
31 llama.cpp Run
Granite 4.1 8B 8.79B Q8_0 8.7 GB 1.25 GB 10.5 GB
0.1 GB free
32 llama.cpp Run
LFM2.5 8B-A1B 8.47B MoE / 1.5B act Q8_0 8.4 GB 0.09 GB 9.1 GB
1.5 GB free
72 llama.cpp Run
Fara 7B 8.29B Q8_0 8.2 GB 0.44 GB 9.2 GB
1.4 GB free
34 llama.cpp Run
Qwen3 8B 8.19B Q8_0 8.1 GB 1.13 GB 9.8 GB
0.8 GB free
34 llama.cpp Run
Llama 3.1 8B Instruct 8.03B Q8_0 7.9 GB 1.00 GB 9.5 GB
1.1 GB free
35 llama.cpp Run
Olmo 3 7B Instruct 7.3B Q8_0 7.2 GB 2.50 GB 10.3 GB
0.3 GB free
38 llama.cpp Run

Won't fit in VRAM

47 models · some are possible with CPU offload
ModelParamsQuantWeights +KV 8KTotal Over budget Tok/sRuntime
gpt-oss 20B 20.9B MoE / 3.6B act MXFP4 10.8 GB 0.19 GB 11.6 GB +1.0 GB ~35 llama.cpp Why
Qwen3 14B 14.8B Q8_0 14.6 GB 1.25 GB 16.5 GB +5.9 GB ~5.2 llama.cpp Why
DeepSeek-R1-Distill-Qwen 14B 14.8B Q8_0 14.6 GB 1.50 GB 16.7 GB +6.1 GB ~5.0 llama.cpp Why
Qwen2.5-Coder 14B 14.8B Q8_0 14.6 GB 1.50 GB 16.7 GB +6.1 GB ~5.0 llama.cpp Why
Phi-4 14B 14.7B Q8_0 14.5 GB 1.56 GB 16.7 GB +6.1 GB ~5.0 llama.cpp Why
Ministral 3 14B 13.9B Q8_0 13.8 GB 1.25 GB 15.6 GB +5.0 GB ~5.9 llama.cpp Why
Gemma 3 12B 12.2B Q8_0 12.1 GB 0.81 GB 13.5 GB +2.9 GB ~8.9 llama.cpp Why
Mistral NeMo 12B 12.2B Q8_0 12.1 GB 1.25 GB 13.9 GB +3.3 GB ~8.1 llama.cpp Why
Gemma 4 12B 12B Q8_0 11.9 GB 0.81 GB 13.3 GB +2.7 GB ~9.3 llama.cpp Why
Kimi K3 2.8T-A104B New 2780B MoE / 104B act Q8_0 2750.9 GB 0.21 GB 2751.7 GB +2741.1 GB ~0.1 llama.cpp Why
Qwen3.8 2.4T-A95B New 2446B MoE / 95B act Q8_0 2420.4 GB 0.72 GB 2421.7 GB +2411.1 GB ~0.1 llama.cpp Why
DeepSeek V4 Pro 1.6T-A49B New 1650B MoE / 49B act Q8_0 1632.7 GB 0.54 GB 1633.9 GB +1623.3 GB ~0.3 llama.cpp Why
Kimi K2.6 1T-A32B 1027B MoE / 32B act Q8_0 1016.2 GB 0.54 GB 1017.4 GB +1006.8 GB ~0.4 llama.cpp Why
GLM-5.2 744B-A40B 753B MoE / 40B act Q8_0 745.1 GB 0.69 GB 746.4 GB +735.8 GB ~0.4 llama.cpp Why
DeepSeek-R1 671B 671B MoE / 37B act Q8_0 664.0 GB 0.54 GB 665.1 GB +654.5 GB ~0.4 llama.cpp Why
Qwen3.5 397B-A17B 403B MoE / 17B act Q8_0 398.8 GB 0.23 GB 399.6 GB +389.0 GB ~0.8 llama.cpp Why
Ornith 1.5 397B-A17B New this week 397B MoE / 17B act Q8_0 392.8 GB 0.23 GB 393.7 GB +383.1 GB ~0.8 llama.cpp Why
DeepSeek V4 Flash 284B-A13B New 284B MoE / 13B act Q8_0 281.0 GB 0.38 GB 282.0 GB +271.4 GB ~1.1 llama.cpp Why
Qwen3 235B-A22B 235B MoE / 22B act Q8_0 232.5 GB 1.47 GB 234.6 GB +224.0 GB ~0.7 llama.cpp Why
Qwen3.5 122B-A10B 125B MoE / 10B act Q8_0 123.7 GB 0.19 GB 124.5 GB +113.9 GB ~1.5 llama.cpp Why
Nemotron 3 Super 120B-A12B 124B MoE / 12B act Q8_0 122.7 GB 0.06 GB 123.4 GB +112.8 GB ~1.3 llama.cpp Why
Ling 3.0 Flash 124B-A5B New 124B MoE / 5.1B act Q8_0 122.7 GB 0.06 GB 123.4 GB +112.8 GB ~3.0 llama.cpp Why
Mistral Small 4 119B-A6B 119B MoE / 6.5B act Q8_0 117.8 GB 0.18 GB 118.5 GB +107.9 GB ~2.3 llama.cpp Why
gpt-oss 120B 117B MoE / 5.1B act MXFP4 60.5 GB 0.29 GB 61.4 GB +50.8 GB ~6.1 llama.cpp Why
Llama 4 Scout 109B-A17B 109B MoE / 17B act Q8_0 107.9 GB 1.50 GB 110.0 GB +99.4 GB ~0.9 llama.cpp Why
Llama 3.3 70B Instruct 70.6B Q8_0 69.9 GB 2.50 GB 73.0 GB +62.4 GB ~0.6 llama.cpp Why
DeepSeek-R1-Distill-Llama 70B 70.6B Q8_0 69.9 GB 2.50 GB 73.0 GB +62.4 GB ~0.6 llama.cpp Why
Qwen3.6 35B-A3B 35.9B MoE / 3.3B act Q8_0 35.5 GB 0.16 GB 36.3 GB +25.7 GB ~5.6 llama.cpp Why
Ornith 1.5 35B-A3B New this week 35.9B MoE / 3.3B act Q8_0 35.5 GB 0.16 GB 36.3 GB +25.7 GB ~5.6 llama.cpp Why
LLM-jp 4 33B Thinking New this week 33.2B Q8_0 32.9 GB 2.00 GB 35.5 GB +24.9 GB ~1.4 llama.cpp Why
Qwen3 32B 32.8B Q8_0 32.5 GB 2.00 GB 35.1 GB +24.5 GB ~1.4 llama.cpp Why
DeepSeek-R1-Distill-Qwen 32B 32.8B Q8_0 32.5 GB 2.00 GB 35.1 GB +24.5 GB ~1.4 llama.cpp Why
Qwen2.5-Coder 32B 32.8B Q8_0 32.5 GB 2.00 GB 35.1 GB +24.5 GB ~1.4 llama.cpp Why
Olmo 3.1 32B Instruct 32.2B Q8_0 31.9 GB 1.25 GB 33.7 GB +23.1 GB ~1.5 llama.cpp Why
Nemotron 3.5 Lightning 30B-A3B New 31.6B MoE / 3.2B act Q8_0 31.3 GB 0.05 GB 31.9 GB +21.3 GB ~6.1 llama.cpp Why
Gemma 4 31B 31.3B Q8_0 31.0 GB 2.03 GB 33.6 GB +23.0 GB ~1.5 llama.cpp Why
GLM-4.7-Flash 30B-A3B 31.2B MoE / 3B act Q8_0 30.9 GB 0.41 GB 31.9 GB +21.3 GB ~6.4 llama.cpp Why
Qwen3 30B-A3B 30.5B MoE / 3.3B act Q8_0 30.2 GB 0.75 GB 31.5 GB +20.9 GB ~5.8 llama.cpp Why
Qwen3 Coder 30B-A3B 30.5B MoE / 3.3B act Q8_0 30.2 GB 0.75 GB 31.5 GB +20.9 GB ~5.8 llama.cpp Why
Muse Glimmer 30B New 29.8B Q8_0 29.5 GB 0.18 GB 30.3 GB +19.7 GB ~1.7 llama.cpp Why
Granite 4.1 30B 28.9B Q8_0 28.6 GB 2.00 GB 31.2 GB +20.6 GB ~1.7 llama.cpp Why
Qwen3.8 27B New this week 27.8B Q8_0 27.5 GB 0.50 GB 28.6 GB +18.0 GB ~1.9 llama.cpp Why
Qwen3.6 27B 27.8B Q8_0 27.5 GB 0.50 GB 28.6 GB +18.0 GB ~1.9 llama.cpp Why
Gemma 3 27B 27.4B Q8_0 27.1 GB 1.03 GB 28.7 GB +18.1 GB ~1.9 llama.cpp Why
Gemma 4 26B-A4B 26.5B MoE / 3.8B act Q8_0 26.2 GB 0.51 GB 27.3 GB +16.7 GB ~5.4 llama.cpp Why
Devstral Small 2 24B 24B Q8_0 23.7 GB 1.25 GB 25.6 GB +15.0 GB ~2.2 llama.cpp Why
Mistral Small 3.2 24B 23.6B Q8_0 23.4 GB 1.25 GB 25.2 GB +14.6 GB ~2.3 llama.cpp Why

Totals = quantised weights + f16 KV cache at 8K tokens + 0.6 GB runtime overhead, against 10.6 GB usable VRAM (12 GB less display and driver reserve). Tokens per second are estimated from 456 GB/s peak memory bandwidth at batch 1 — not measured. Read the method.