Is IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF good for agentic (tool-using) tasks?

In our benchmarks, IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF scores 83.3/100 for agentic workflows. It runs at about 105.9 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MLX-oQ4e-MTP is faster (~123.7 tok/s) and still scores well for agentic workflows (84.4/100).

How fast is IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF for local inference?

Across 7 community benchmark runs, IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF reaches up to 110.0 tok/s and averages 105.9 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.

How much memory does IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF need?

The leanest observed configuration used about 12.7 GB of memory.

Which tools have been used to run IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF?

Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.

Model benchmarks

IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF local LLM performance

As of October 2026, IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF runs at up to 110.0 tok/s for local inference (best of 7 community benchmark runs across 1 GPU).

llama.cpp

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Model size

35B

Peak speed

110.0 tok/s

Average speed

105.9 tok/s

Avg PP

747.3 tok/s

Min memory

12.7 GB

Max context

32,768 tokens

Avg output / run

30,416 tokens

Avg runtime / run

4m 57s

Avg quality

76.8

Benchmark runs

GPUs tested

Quality by task

Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 7 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.

Task	P5 (low)	Avg	P95 (high)
Overall	68.1	76.8	84.8
Agent Workflow	74.5	83.3	89.0
Code Generation	18.6	53.0	80.6
Role Play & Narrative	81.2	86.9	91.0
Research & Analysis	82.3	84.0	85.9

Performance by hardware and tool

Every hardware/tool/quantization combination IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.

Hardware	Tool	Quant	Best tok/s	Avg tok/s	Memory	Context	Quality	Runs
AMD Radeon RX 9070/9070 XT/9070 GRE	llama.cpp	—	110.0 tok/s	105.9 tok/s	12.7 GB	32,768 tokens	76.8	7

Benchmark runs

All 7 IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF good for agentic (tool-using) tasks?: In our benchmarks, IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF scores 83.3/100 for agentic workflows. It runs at about 105.9 tok/s, so if you want more speed, Tiel-Coder-35B-A3B-MLX-oQ4e-MTP is faster (~123.7 tok/s) and still scores well for agentic workflows (84.4/100).
How fast is IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF for local inference?: Across 7 community benchmark runs, IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF reaches up to 110.0 tok/s and averages 105.9 tok/s, with the fastest results on AMD Radeon RX 9070/9070 XT/9070 GRE.
How much memory does IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF need?: The leanest observed configuration used about 12.7 GB of memory.
Which tools have been used to run IsValorum/Occamy-1.0-APEX-I-NanoPlus-GGUF?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.