Best Local LLMs for Agents

作者: Jakub Rusinowski · 最后更新: 2026年10月15日

Multi-step autonomous loops where the model calls tools, reads results and decides what to do next.

Top pick: Ternary Bonsai 27B

Scores 94/100 for autonomous agents and tool use. 27B parameters, needing about 6.6 GB at Ternary (1.58-bit, ~1.71 bpw), 256K context, Apache 2.0.

Ranked for autonomous agents and tool use

ModelScoreParamsContextLicenceQuality index
1. Ternary Bonsai 27B9427B256KApache 2.0— (estimated)
2. Devstral Small 2 24B91.224B256KApache-2.0— (estimated)
3. Nemotron 3.5 Bolt 74B90.874B256KOpenMDW-1.1— (estimated)
4. Nemotron-Cascade 2 30B-A3B90.632B1MNVIDIA Open Model License— (estimated)
5. Qwen3-Coder 30B-A3B (MoE)89.931B256KApache-2.0— (estimated)
6. Seed-OSS-36B-Instruct89.536B512KApache-2.0— (estimated)

Frontier open-weight models (multi-GPU / private cloud)

These also score for autonomous agents and tool use, but none runs on a single consumer GPU or workstation, so they are listed apart rather than ranked against the local picks above.

Best pick for your memory budget

The strongest model overall is rarely the right answer — what matters is the strongest model that fits the memory you have. These picks are re-ranked per tier, so each one uses its budget rather than simply being small.

MemoryTypical hardwareRecommended models
8 GBRTX 4060, RTX 3070, base MacBook AirTernary Bonsai 27B (94)
Ternary Bonsai 2 27B (88.8)
GLM-6 9B (86.8)
12 GBRTX 3060 12 GB, RTX 5070Ternary Bonsai 27B (93.5)
Ternary Bonsai 2 27B (88.4)
GLM-6 9B (86)
16 GBRTX 5080, RTX 4080, RX 9070 XTDevstral Small 2 24B (92.6)
Ternary Bonsai 27B (92.2)
Ternary Bonsai 2 27B (87)
24 GBRTX 4090, RTX 3090, RX 7900 XTXNemotron 3.5 Bolt 74B (93.1)
Nemotron-Cascade 2 30B-A3B (92.8)
Devstral Small 2 24B (92.6)
48 GBRTX 6000 Ada, MacBook Pro M4 Max 48 GBNemotron 3.5 Bolt 74B (91.9)
Nemotron-Cascade 2 30B-A3B (91)
Seed-OSS-36B-Instruct (90.5)
128 GB+Mac Studio, DGX Spark, multi-GPUDevstral-2 123B (95.2)
Qwen3.8-Flash-Next (93.9)
Qwen3-Coder-Next (80B-A3B MoE) (93.1)

How this ranking works

Reliable structured tool calls are a hard gate here, not a bonus: a model with no tool-use tag is excluded regardless of score. Coding weight is high (40%) because tool arguments are effectively code. Context needs match RAG — an agent loop accumulates every observation it has ever seen.

Worked example — Ternary Bonsai 27B: capability 89.3 × 0.427, quality 87.7 × 0.251, context 100 × 0.163, license 70 × 0.045, accessibility 100 × 0.113 + 3 tag bonus (agents).

Requirements applied: context floor 32,768 tokens (ideal 262,144), quality floor 60, licence weight 0.4, latency weight 0.6. Tool-calling capability is required.

Running autonomous agents and tool use locally

FAQ

What is the best local LLM for autonomous agents and tool use?

Ternary Bonsai 27B, scoring 94/100 against this workload's published requirements. 54 models qualified.

What hardware do I need for autonomous agents and tool use?

A credible answer starts at 8 GB of memory. Larger budgets unlock materially stronger models — the table above lists the best pick at each tier.

How were these models ranked?

Reliable structured tool calls are a hard gate here, not a bonus: a model with no tool-use tag is excluded regardless of score. Coding weight is high (40%) because tool arguments are effectively code. Context needs match RAG — an agent loop accumulates every observation it has ever seen.

Hardware for This Workload

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