Is Llama-3.1-Tulu-3-8B-Q4_K_M good for coding?

In our benchmarks, Llama-3.1-Tulu-3-8B-Q4_K_M scores 29.1/100 for coding. It runs at about 42.2 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 Llama-3.1-Tulu-3-8B-Q4_K_M good for agentic (tool-using) tasks?

In our benchmarks, Llama-3.1-Tulu-3-8B-Q4_K_M scores 47.1/100 for agentic workflows. It runs at about 42.2 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 Llama-3.1-Tulu-3-8B-Q4_K_M for local inference?

Across 10 community benchmark runs, Llama-3.1-Tulu-3-8B-Q4_K_M reaches up to 43.9 tok/s and averages 42.2 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.

How much memory does Llama-3.1-Tulu-3-8B-Q4_K_M need?

The leanest observed configuration used about 7.2 GB of memory (quantizations tested: Q4_K_M).

Which tools have been used to run Llama-3.1-Tulu-3-8B-Q4_K_M?

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

Model benchmarks

Llama-3.1-Tulu-3-8B-Q4_K_M local LLM performance

As of October 2026, Llama-3.1-Tulu-3-8B-Q4_K_M runs at up to 43.9 tok/s for local inference (best of 10 community benchmark runs across 1 GPU).

llama.cppQ4_K_M

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Model size

Peak speed

43.9 tok/s

Average speed

42.2 tok/s

Avg PP

756.8 tok/s

Min memory

7.2 GB

Max context

32,768 tokens

Avg output / run

4,397 tokens

Avg runtime / run

1m 48s

Avg quality

47.3

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	40.3	47.3	55.6
Agent Workflow	19.5	47.1	70.4
Code Generation	16.9	29.1	38.4
Role Play & Narrative	58.2	62.7	66.9
Research & Analysis	38.6	50.3	66.6

Performance by hardware and tool

Every hardware/tool/quantization combination Llama-3.1-Tulu-3-8B-Q4_K_M 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	Q4_K_M	43.9 tok/s	42.2 tok/s	7.2 GB	32,768 tokens	47.3	10

Benchmark runs

All 10 Llama-3.1-Tulu-3-8B-Q4_K_M runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.

Open run page →

Frequently asked questions

Is Llama-3.1-Tulu-3-8B-Q4_K_M good for coding?: In our benchmarks, Llama-3.1-Tulu-3-8B-Q4_K_M scores 29.1/100 for coding. It runs at about 42.2 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 Llama-3.1-Tulu-3-8B-Q4_K_M good for agentic (tool-using) tasks?: In our benchmarks, Llama-3.1-Tulu-3-8B-Q4_K_M scores 47.1/100 for agentic workflows. It runs at about 42.2 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 Llama-3.1-Tulu-3-8B-Q4_K_M for local inference?: Across 10 community benchmark runs, Llama-3.1-Tulu-3-8B-Q4_K_M reaches up to 43.9 tok/s and averages 42.2 tok/s, with the fastest results on NVIDIA GeForce RTX 4060 Laptop GPU.
How much memory does Llama-3.1-Tulu-3-8B-Q4_K_M need?: The leanest observed configuration used about 7.2 GB of memory (quantizations tested: Q4_K_M).
Which tools have been used to run Llama-3.1-Tulu-3-8B-Q4_K_M?: Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.