Model benchmarks
Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 local LLM performance
As of November 2026, Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 runs at up to 61.7 tok/s for local inference (best of 2 community benchmark runs across 2 GPUs).
Model size
Unknown
Peak speed
61.7 tok/s
Average speed
47.8 tok/s
Avg PP
118.8 tok/s
Min memory
77.0 GB
Max context
65,536 tokens
Avg output / run
42,452 tokens
Avg runtime / run
14m 15s
Avg quality
77.3
Benchmark runs
2
GPUs tested
2
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 2 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 | 67.5 | 77.3 | 87.2 |
| Agent Workflow | 85.7 | 88.9 | 92.2 |
| Code Generation | 19.5 | 50.9 | 82.3 |
| Role Play & Narrative | 87.3 | 87.4 | 87.5 |
| Research & Analysis | 77.2 | 82.1 | 87.0 |
Performance by hardware and tool
Every hardware/tool/quantization combination Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 has been benchmarked on, ranked by peak token generation speed. Last updated November 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| AMD Radeon RX 7900 XTX | OpenAI-compatible | — | 61.7 tok/s | 61.7 tok/s | 77.0 GB | 65,536 tokens | 88.3 | 1 |
| AMD Radeon RX 7800 XT | llama.cpp | — | 33.9 tok/s | 33.9 tok/s | n/a | 8,192 tokens | 66.4 | 1 |
Benchmark runs
All 2 Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 good for coding?
- In our benchmarks, Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 scores 50.9/100 for coding. It runs at about 47.8 tok/s, so if you want more speed, local-qwen is faster (~60.2 tok/s) and still scores well for coding (84.8/100).
- Is Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 good for agentic (tool-using) tasks?
- In our benchmarks, Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 scores 88.9/100 for agentic workflows. It runs at about 47.8 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 for local inference?
- Across 2 community benchmark runs, Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 reaches up to 61.7 tok/s and averages 47.8 tok/s, with the fastest results on AMD Radeon RX 7900 XTX.
- How much memory does Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033 need?
- The leanest observed configuration used about 77.0 GB of memory.
- Which tools have been used to run Qwen3.8-Flash-Next-Avalon-IQ3KS-00001-of-00033?
- Benchmarks were submitted using OpenAI-compatible, llama.cpp. Results are community-contributed and updated as new runs arrive.