Is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 good for coding?

In our benchmarks, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 scores 66.1/100 for coding. It runs at about 65.8 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~91.4 tok/s) and still scores well for coding (81.6/100).

Is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 good for agentic (tool-using) tasks?

In our benchmarks, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 scores 81.7/100 for agentic workflows. It runs at about 65.8 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).

How fast is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 for local inference?

Across 10 community benchmark runs, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 reaches up to 67.8 tok/s and averages 65.8 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.

How much memory does NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 need?

The leanest observed configuration used about 6.3 GB of memory (quantizations tested: Q6_K).

Which tools have been used to run NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4?

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

Model benchmarks

NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 local LLM performance

As of October 2026, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 runs at up to 67.8 tok/s for local inference (best of 10 community benchmark runs across 1 GPU).

llama.cppQ6_K

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Model size

Peak speed

67.8 tok/s

Average speed

65.8 tok/s

Avg PP

830.8 tok/s

Min memory

6.3 GB

Max context

32,768 tokens

Avg output / run

7,873 tokens

Avg runtime / run

1m 55s

Avg quality

73.2

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 10 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	67.6	73.2	75.9
Agent Workflow	74.3	81.7	89.3
Code Generation	57.0	66.1	73.0
Role Play & Narrative	68.9	81.3	88.0
Research & Analysis	56.6	63.7	68.3

Performance by hardware and tool

Every hardware/tool/quantization combination NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 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
NVIDIA GeForce RTX 4060 Laptop GPU	llama.cpp	Q6_K	67.8 tok/s	65.8 tok/s	6.3 GB	32,768 tokens	73.2	10

Benchmark runs

All 10 NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 good for coding?: In our benchmarks, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 scores 66.1/100 for coding. It runs at about 65.8 tok/s, so if you want more speed, Nex-N2.5-mini-IQ3_XXS is faster (~91.4 tok/s) and still scores well for coding (81.6/100).
Is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 good for agentic (tool-using) tasks?: In our benchmarks, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 scores 81.7/100 for agentic workflows. It runs at about 65.8 tok/s, so if you want more speed, Nex-N2.5-mini-APEX-Mini is faster (~88.2 tok/s) and still scores well for agentic workflows (91.3/100).
How fast is NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 for local inference?: Across 10 community benchmark runs, NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 reaches up to 67.8 tok/s and averages 65.8 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.
How much memory does NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4 need?: The leanest observed configuration used about 6.3 GB of memory (quantizations tested: Q6_K).
Which tools have been used to run NVIDIA-Nemotron-3.5-Bolt-E4B-Q6_K-DFLASH-NVFP4?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.