Is Spark-X2.6-4B-Q8_0 good for coding?

In our benchmarks, Spark-X2.6-4B-Q8_0 scores 52.1/100 for coding. It runs at about 42.4 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).

Is Spark-X2.6-4B-Q8_0 good for agentic (tool-using) tasks?

In our benchmarks, Spark-X2.6-4B-Q8_0 scores 72.2/100 for agentic workflows. It runs at about 42.4 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 Spark-X2.6-4B-Q8_0 for local inference?

Across 10 community benchmark runs, Spark-X2.6-4B-Q8_0 reaches up to 46.2 tok/s and averages 42.4 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.

How much memory does Spark-X2.6-4B-Q8_0 need?

The leanest observed configuration used about 6.5 GB of memory (quantizations tested: Q8_0).

Which tools have been used to run Spark-X2.6-4B-Q8_0?

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

Model benchmarks

Spark-X2.6-4B-Q8_0 local LLM performance

As of October 2026, Spark-X2.6-4B-Q8_0 runs at up to 46.2 tok/s for local inference (best of 10 community benchmark runs across 1 GPU).

llama.cppQ8_0

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Model size

Peak speed

46.2 tok/s

Average speed

42.4 tok/s

Avg PP

1063.6 tok/s

Min memory

6.5 GB

Max context

32,768 tokens

Avg output / run

7,935 tokens

Avg runtime / run

3m 16s

Avg quality

62.2

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	54.5	62.2	68.2
Agent Workflow	54.2	72.1	85.4
Code Generation	38.1	52.1	67.8
Role Play & Narrative	62.4	69.3	75.6
Research & Analysis	39.6	55.3	66.5

Performance by hardware and tool

Every hardware/tool/quantization combination Spark-X2.6-4B-Q8_0 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	Q8_0	46.2 tok/s	42.4 tok/s	6.5 GB	32,768 tokens	62.2	10

Benchmark runs

All 10 Spark-X2.6-4B-Q8_0 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is Spark-X2.6-4B-Q8_0 good for coding?: In our benchmarks, Spark-X2.6-4B-Q8_0 scores 52.1/100 for coding. It runs at about 42.4 tok/s, so if you want more speed, nvidia/Qwen3.8-27B-NVFP4 is faster (~63.3 tok/s) and still scores well for coding (82.7/100).
Is Spark-X2.6-4B-Q8_0 good for agentic (tool-using) tasks?: In our benchmarks, Spark-X2.6-4B-Q8_0 scores 72.2/100 for agentic workflows. It runs at about 42.4 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 Spark-X2.6-4B-Q8_0 for local inference?: Across 10 community benchmark runs, Spark-X2.6-4B-Q8_0 reaches up to 46.2 tok/s and averages 42.4 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.
How much memory does Spark-X2.6-4B-Q8_0 need?: The leanest observed configuration used about 6.5 GB of memory (quantizations tested: Q8_0).
Which tools have been used to run Spark-X2.6-4B-Q8_0?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.